Smartphone photography has undergone a revolution in the past few years thanks to advances in artificial intelligence (AI) and computational imaging. Flagship phones in 2024–2025 like Apple’s iPhone 15 Pro Max, Samsung’s Galaxy S24 Ultra, and Google’s Pixel 8 Pro rely on powerful algorithms and neural networks to produce stunning photos and videos. These devices use computational photography techniques – from sophisticated HDR and Night modes to portrait bokeh and super-res zoom – to overcome physical limitations of small camera sensors. The result is that AI-driven image processing pipelines now play as big a role as hardware in defining a phone camera’s qualitydxomark.com ↗️. In this article, we’ll explore how Apple, Samsung, and Google each implement these AI techniques, compare their approaches, and see how algorithms are shaping the photos and videos we capture on mobile devices.
The Rise of Computational Photography and AI
Mobile cameras have embraced computational photography to deliver superior dynamic range, low-light performance, and detail. Computational photography refers to merging multiple exposures and applying algorithmic corrections to create a single improved image. Today’s phones go even further by using AI and machine learning models for scene recognition, segmentation, and image enhancement. For example, Apple’s Camera app uses on-device neural networks to identify elements like people and skies in a scene and adjust each appropriately – a technique known as semantic rendering. Apple’s own research highlights that features like Smart HDR, Photographic Styles, and even noise reduction all leverage pixel-level image segmentation (e.g. separate masks for faces, skin, and sky) to apply targeted adjustmentsmachinelearning.apple.com ↗️. Google and Qualcomm have taken a similar approach: the Snapdragon AI “Cognitive ISP” in phones like the Galaxy S23/S24 can recognize different elements (faces, hair, background) in real time and optimize them individuallytomsguide.com ↗️. In short, modern smartphone cameras continuously analyze what you’re shooting – and then algorithms decide how to capture and process the shot for the best result.
This AI-driven pipeline is present whether or not it’s marketed overtly. Google tends to advertise its “AI camera” features (the Pixel 8 Pro’s software is literally called the Pixel AI camera system on the Google Storestore.google.com ↗️), while Apple doesn’t brand features as “AI” even though machine learning is deeply integratedtechradar.com ↗️. Regardless of branding, all flagship cameras now heavily rely on neural network models and specialized imaging hardware (like Apple’s Neural Engine, Google’s Tensor G3, or Qualcomm’s Hexagon NPU) to perform trillions of operations on each photo in millisecondsistyle.ae ↗️dxomark.com ↗️. The sections below break down how AI algorithms enhance specific aspects of mobile photography – HDR, night shots, portraits, zoom, and video – and how the big three smartphone makers implement them.
Smart HDR: Expanding Dynamic Range with AI
One of the earliest and most influential computational techniques is HDR (High Dynamic Range) imaging. HDR merges multiple exposures to better capture both bright highlights and dark shadows in high-contrast scenes. Today’s flagships all do HDR by default – Apple’s Smart HDR 5 on the iPhone 15 series, Google’s classic HDR+ (augmented by a new Ultra HDR format in Android), and Samsung’s Super HDR on the Galaxy – but they each have their own spin on it.
Apple’s approach: Smart HDR on recent iPhones uses bracketed frames combined with intelligent processing to produce balanced photos. The iPhone 15 Pro Max, for instance, continually captures a burst of images and uses the best combination when you tap the shutter. Apple’s pipeline leverages Deep Fusion and the Photonic Engine to optimize detail and tone before merging the frames. Deep Fusion (introduced with iPhone 11) was Apple’s first implementation where a neural engine generates the final 12MP image by analyzing multiple frames at a pixel level. gizmodo.com ↗️ In a typical Smart HDR/Deep Fusion shot, an iPhone might use up to 9 separate exposures: for example, four short-exposure frames, four medium frames (captured before you press shutter), and then one longer exposure when you tap the shutter. These are intelligently merged – the best short-exposure image (which preserves fine detail) is combined with a “synthetic long” exposure created by merging the other frames. Apple’s A-series Neural Engine analyzes detail at multiple frequency bands and fuses the images pixel by pixel, applying selective noise reduction and tone mapping. All this happens in under a second on-device. The result is that Smart HDR 5 can render bright skies, midtones, and shadows with remarkable balance and natural look, without user effort. Apple claims and reviewers confirm that skin tones and colors remain very lifelike on iPhone HDR images. apple.com ↗️ By using AI segmentation, the iPhone can separately optimize parts of the scene – e.g. ensuring a face is well-lit without blowing out a bright sky. This gives Apple’s photos a very natural dynamic range and contrast. One downside noted is that Apple sometimes prioritizes realism over extreme HDR effect, so shadow areas might remain a touch darker (to preserve mood) compared to some competitors.
Google’s approach: Google pioneered computational HDR with its HDR+ algorithm, which since the Nexus/early Pixel days has used burst photography of underexposed frames. Classic HDR+ takes up to ~9 rapid-fire short exposures and aligns and merges them to extend dynamic range and reduce noise. research.google ↗️ This avoids blowing out highlights (by underexposing frames) and then recombining to lift shadows. Starting with the Pixel 4, Google augmented this with HDR+ with Bracketing, which adds longer exposure frames to the mix. In high dynamic range scenes or very low light, the Pixel can capture a series of short frames plus a few long frames after shutter press. For example, in Night Sight (Pixel’s extreme low-light mode), the Pixel 4/5 would capture 12 short exposures and 3 long exposures and merge all 15 into one image. This greatly improves shadow detail and signal-to-noise ratio. Even in normal daylight HDR, the latest Pixels use a blend of short and long frames to cover the tonal range. Google’s strength has been in software merge and tone mapping: HDR+ is tuned to maximize detail and vibrant contrast, sometimes at the expense of looking a bit unnatural. Pixel phones often produce punchier HDR images with high contrast – for instance, shadows will be opened up and skies kept rich. In a direct comparison, one reviewer noted the Pixel 8 Pro produced the “most striking results” in a bright, cloudy scene, with strong contrast and bold colors, whereas the iPhone’s output was more balanced and true-to-life. techradar.com ↗️ Google’s Ultra HDR format, introduced in Android 14 and used on Pixel 8, even captures high-bit-depth HDR images with extra highlight information (a gain map), though this requires compatible displays/apps to fully appreciate. DXOMARK tests found the Pixel 8 Pro’s color rendering and exposure in challenging lighting to be excellent, even slightly ahead of the iPhone in some HDR scenarios, thanks to Google’s tuning. dxomark.com ↗️ However, the Pixel’s aggressive tone mapping can sometimes introduce a slight color cast (e.g. a magenta tint in some clouds or backgrounds) in certain scenes. Overall, Google leans on on-device AI (via the Tensor G3 chip) to do things like segment the sky vs. other objects and apply HDR tone mapping selectively, similar in concept to Apple. The result is that Pixel’s HDR+ tends to produce very dynamic, crisp images that impress in blind comparisons.
Samsung’s approach: Samsung’s Galaxy S24 Ultra also implements automatic multi-frame HDR on every shot, branded as Adaptive Pixel and Super HDR. The S24 Ultra’s camera app captures multiple exposures with varying ISO and shutter whenever it detects a high-contrast scene, then blends them. Samsung has been more tight-lipped on the exact frame counts, but the Snapdragon platform it uses enables real-time semantic segmentation (as on Pixel) for HDR processing. The Galaxy S23/S24’s AI-powered ISP can recognize different areas (sky, faces) and adjust tone mapping accordingly in the merged image. Samsung typically goes for a bright and vivid HDR look – lifting shadows quite aggressively and maintaining bright highlights. In practice, the Galaxy S24 Ultra often yields brighter overall images than either the iPhone or Pixel in backlit scenes. In a comparison, the S24 Ultra was able to “boost shadows and draw out a little more detail” in a challenging high-contrast shot, clearly illuminating dark areas (like details in building shadows) where the Pixel left more darkness. This means Samsung’s HDR can make every part of the photo visible, though sometimes at the cost of realism – e.g. it might blow out small bright highlights a bit or flatten the contrast. Still, Samsung has improved color accuracy in HDR scenes on the S24. Testers report neutral white balance and pleasant color rendering in most conditions for the S24 Ultra. dxomark.com ↗️ The use of AI is evident in Samsung’s marketing: Super HDR and scene optimizer features are said to use “enhanced AI” to ensure even tricky lighting is handled well. The Galaxy S24 can even shoot HDR10+ images and video (an HDR format with dynamic metadata), leveraging its multi-frame capture for richer color and contrast on compatible displays.
Overall HDR outcome: All three brands have converged on delivering excellent dynamic range by fusing multiple frames with AI. The differences are in tone mapping philosophy: Apple tends toward a balanced, true-to-eye HDR (avoiding an over-processed look), Google tends toward a high-contrast “Wow” look while preserving realistic skin tones, and Samsung tends toward brightening everything for clarity. Notably, all use AI-driven segmentation – for example, Apple explicitly uses person masks and sky masks in Smart HDR to ensure each is optimally exposed. Qualcomm’s tech in Samsung phones similarly identifies elements like sky vs. human vs. text and applies localized enhancements. This means a bright sky can be processed differently from a shadow on a face within the same photo. The result is HDR photos that are far better than those from just a few years ago, making blown-out or underexposed shots a rarity. Each of these flagship phones nails exposure so well that DXOMARK’s rankings for exposure and contrast are uniformly high, with iPhone 15 Pro Max noted for “accurate exposure, even in night shots” and the S24 Ultra likewise praised for accurate target exposure down to low light. In summary, AI-powered HDR has greatly expanded what’s possible in tricky lighting, delivering images that preserve highlights and shadows in ways traditional cameras struggle to match.
Night Mode: Illuminating the Darkness with Neural Networks
Perhaps the most impressive showcase of AI in phone cameras is the Night Mode. Taking photos in very low light (think indoors at night or city streets after dark) used to result in dark, noisy images. But modern phones can produce bright, detailed night shots that often look like they were taken with much more light. This is achieved through multi-frame stacking, long exposures, and intelligent noise reduction – all guided by AI algorithms.
Google’s Night Sight: When Google introduced Night Sight on the Pixel, it amazed users by making night scenes appear bright without a flash. The Pixel’s approach is to capture a burst of many frames over a second or more, then align and merge them to increase exposure and reduce noise. As noted, the original Night Sight took up to 15 short frames research.google ↗️. Newer Pixels use exposure bracketing in Night Sight: for example, the Pixel 8 Pro can capture a series of short exposures plus several longer exposures (up to ~0.5s each) in one Night Sight shot. Google’s algorithm then merges these frames, leveraging the short frames to keep moving subjects sharp, and the long frames to pull out shadow detail. The heavy lifting is done by the Pixel’s AI pipeline – noise reduction is learned from data, meaning the device’s neural engine helps distinguish noise from detail and remove grain while preserving textures. The result is often startling: Night Sight photos come out clear, with well-balanced brightness and color. Skin tones remain accurate and the overall scene retains a fairly natural look with much lower noise than a single exposure could ever achieve. According to DXOMARK, the Pixel 8 Pro ranks among the best for low-light shooting, with accurate exposure and nicely preserved color in dark conditions dxomark.com ↗️. Testers did note that when peeping at fine details, the Pixel’s noise reduction could sometimes blur very fine textures more than some rivals. Still, the consensus is that Google’s Night Sight sets a high bar. It essentially turns night into day when desired, though users can also tap to expose more conservatively if they want a darker mood. Google even extended Night Sight to Night Sight Portraits (combining low-light multi-frame with a depth effect) and Astrophotography mode, which on a tripod can stack dozens of frames over 4 minutes to capture stars and night skies. All of this relies on the Tensor chip’s AI to align frames (even with some hand shake), cancel out noise, and even boost colors that our eyes barely see in the dark. For example, the Pixel can make a dim scene look vibrant yet still real, a balancing act of AI-based tone mapping.
Apple’s Night Mode: Apple introduced Night Mode starting with the iPhone 11, and it has improved further through the iPhone 15 generation. On an iPhone, Night Mode automatically activates when the scene is dark, suggesting a certain exposure time (e.g. 1 second up to 3 seconds handheld, or even 10+ seconds on a tripod). During that capture time, the camera is actually taking multiple frames: it typically takes a long base exposure to brighten the scene, and intersperses shorter exposures to freeze any motion. The Photonic Engine then kicks in to merge these frames intelligently. Apple’s pipeline benefits from the large sensors and fast apertures in recent iPhones, but also from AI-driven image fusion. When Night Mode is engaged, Apple applies Deep Fusion at an earlier stage, working with the uncompressed image data to maximize detail in low light istyle.ae ↗️. The Photonic Engine uses pixel-by-pixel machine learning to enhance lighting and detail in dim conditions. The result on the iPhone 15 Pro/Pro Max is sharper details and more vivid colors in low light (compared to previous iPhones), as Apple noted in its announcements apple.com ↗️. One key advantage for Apple is the LiDAR scanner on Pro models, which aids autofocus and depth sensing in the dark. Thanks to LiDAR, the iPhone can achieve Night Mode portraits with accurate depth mapping (so your subject is sharp against nicely blurred backgrounds even in near darkness). In terms of image character, iPhone Night mode tends to favor a natural look with controlled brightness. It will brighten a scene significantly, but often a Pixel might still produce a slightly brighter image in extreme dark. In exchange, the iPhone image might retain a bit more contrast or true-to-scene lighting (i.e. if it was really dark, the iPhone shot won’t look like daytime; it respects the night mood). Reviewers have found that iPhone’s Night mode produces very clean images with excellent color fidelity – for example, the iPhone 15 Pro Max was praised for accurate exposure even in night shots and pleasing color rendering of nighttime scenes. However, Apple can sometimes leave a bit of luminance noise in very dark areas (rather than smearing them), which some users actually prefer as it preserves detail. Anecdotally, some photographers note the iPhone’s night photos look more balanced whereas Pixels can look a bit HDR-ish at night. Apple’s use of adaptive bracketing (taking longer frames when the phone is steady vs. shorter if it detects motion or handshake) means it tries to optimize for a sharp yet bright result. If you hold still or use a tripod, it will gladly take a longer composite to reduce noise further. Overall, Apple’s Night Mode has caught up to Google’s – and in some situations like people in low light, the iPhone may produce more realistic skin tones versus the Pixel’s sometimes warm cast. In fact, Tom’s Guide found that in a high-contrast night scene, the Pixel 8 Pro handled bright light sources better (string lights weren’t blown out) but the Galaxy/Apple made the scene brighter tomsguide.com ↗️. And DXOMARK noted the iPhone 15 Pro Max maintains accurate exposure at night where some competitors sacrifice highlights or introduce more noise.
Samsung’s Nightography: Samsung markets its low-light computation as Nightography – essentially their Night Mode which has been heavily refined in the S23 and S24 generation. With the Galaxy S24 Ultra’s large 200MP sensor (which bins to 12MP for huge effective pixels) and multi-frame processing, Samsung can often capture night shots very quickly. The phone typically takes several frames (including some very long ones if on Night mode) and merges them to boost shadows. Samsung leans toward making night photos look bright and colorful, sometimes even more so than reality. This can be a pro or con depending on taste. Tom’s Guide noted that Samsung has “consistently proven it’s the one to beat” in basic low-light brightness – the S24 Ultra often produced the brightest image of the scene tomsguide.com ↗️. For instance, the Galaxy might turn a nearly dark garage into a well-lit-looking photo where the Pixel kept it a bit darker. However, Samsung’s algorithms sometimes trade off highlight detail: in a head-to-head, the S24 Ultra made a dim scene brighter, but small bright highlights (like lights) blew out more compared to the Pixel. In that comparison, the Pixel’s shot was dimmer overall but preserved the lighting nuance (the Pixel won the “high-contrast low light” category for a more realistic balance). Samsung has undoubtedly improved its AI noise reduction – the S24 Ultra uses AI-powered multi-frame noise reduction to clean up dark areas. DXOMARK noted the S24 Ultra had well-controlled noise in bright light and acceptable noise in low light, though not as low as the iPhone or Pixel in extreme dark. One strength for Samsung is leveraging that 200MP sensor in low light: the phone can do a 16-to-1 pixel bin, effectively a huge pixel size for gathering light. In very dark scenes, it can switch to using the full sensor in a binned mode and then apply multi-frame on top, resulting in images that are bright and relatively detailed. Enhanced AI Nightography, as Samsung calls it, also works in conjunction with their Scene Optimizer – it can recognize a night cityscape vs. a portrait at night and adjust processing. Samsung even has a dedicated Night portrait mode (using AI segmentation to ensure the subject is clear and background nicely blurred and denoised). Overall, Samsung’s night shots tend to be crowd-pleasers – bright, saturated, with perhaps a bit less fine detail when viewed up close compared to Apple/Google. They’re great for sharing on social media where a punchy look is preferred. And with features like Nightography Zoom (new on S24 Ultra), Samsung claims you can even get bright zoomed shots at night using AI stabilization and frame stacking shopmoment.com ↗️.
In summary, AI-driven Night modes have fundamentally changed mobile photography. These flagships can capture handheld night photos that were impossible a few years ago. Each device has its tuning: Pixel aims for a “see in the dark” clarity while keeping things relatively truthful, iPhone aims for a natural but clean look with excellent detail, and Samsung often aims for a bright and vibrant night shot. All use similar ingredients – long exposures + multiple frames + neural denoising – but the recipes differ slightly. The good news is that in most casual night shooting scenarios, all three phones will deliver a very good photo. It’s only at the extremes (very low light, moving subjects, mixed lighting) where their approaches diverge. Notably, focus in low light is helped by AI too: Apple’s LiDAR gives it an edge for focus on subjects in near-dark, Google uses AI algorithms for autofocus and can even do things like Motion Mode (blurring moving backgrounds at night creatively), and Samsung’s laser AF plus AI helps it lock focus quicker in dim settings. According to DXOMARK’s scores, the Pixel 8 Pro slightly outscored the iPhone 15 Pro Max in the low-light category overall (likely due to that aggressive noise reduction and exposure) dxomark.com ↗️, but both are among the top, with the S24 Ultra just a bit behind. We’ve reached a point where handheld night photography yields surprisingly detailed results, thanks to AI combining the light from many moments into one image.
Portrait Mode and Bokeh: Depth Mapping with AI
Smartphone Portrait Modes, which blur the background to mimic DSLR-like shallow depth of field, are another area heavily driven by AI algorithms. Creating a convincing bokeh (background blur) effect requires accurately separating the subject (person, pet, or object) from the background – essentially a segmentation or depth estimation problem – and then artistically blurring what’s not the subject. Initially, phones achieved this with dual cameras or dedicated sensors, but now AI does a lot of the heavy lifting, even enabling portrait effects with a single lens.
Segmentation and depth technology: Apple, Google, and Samsung each use a combination of camera hardware and AI for portraits. Apple uses dual cameras (or a single camera plus LiDAR on Pro iPhones) to gather stereo depth data, and also uses neural networks to refine the segmentation mask. As Apple’s research paper explains, they have person segmentation models that can delineate hair, glasses, etc., and even separate multiple people. machinelearning.apple.com ↗️ This feeds into Portrait mode: the iPhone creates a depth map and a high-quality mask of the person, allowing a realistic blur gradient behind them. Google’s Pixel phones famously achieved portrait mode even on single-camera devices (like Pixel 2) by using Dual-Pixel autofocus pixels to get a parallax view for depth. In addition, Google trains AI models to detect human figures and edges to improve the cutout. Samsung typically uses dual cameras (e.g. the telephoto + main) for depth, and in recent models, pure AI for additional refinement (the depth sensors found on older models have been phased out in favor of algorithmic solutions). Across all brands, AI-based semantic segmentation ensures things like stray hairs or complex silhouettes are handled better now than in early portrait modes which often had obvious cut-out artifacts.
Apple’s Portraits: On the iPhone 15 Pro series, Apple introduced a handy feature – you no longer even have to switch to a dedicated Portrait mode. If the camera detects a person, cat, or dog in the view (or if you tap to focus on an object), it automatically captures depth information using the dual pixels or dual lenses. apple.com ↗️ Later, you can turn the photo into a portrait (blurred background) in the Photos app and even adjust the focal point after the fact. The Photonic Engine and Deep Fusion also apply to portraits, giving sharper details on faces and more vibrant colors even in low light portraits. Apple leverages LiDAR on Pro models in dark scenes to improve subject separation for Night Mode portraits. The result: iPhone portrait shots are generally very natural in subject rendering (great skin tones and details thanks to Deep Fusion) and have a pleasing, gradual bokeh that emulates a real camera lens. Apple also offers Portrait Lighting effects (stage light, etc.) which are essentially AI filters using the depth map – these too rely on segmentation quality. According to DXOMARK, the iPhone 15 Pro Max excels in portrait photography, capturing “intricate detail and beautiful skin tones”. dxomark.com ↗️ The improved neural processing means fewer mistakes on things like where hair ends and background begins. Nonetheless, no system is perfect – complex scenes (like a person behind semi-transparent objects or with similar color background) can still confuse edge detection at times. But those cases are rarer now. One bonus of Apple’s approach is the ability to refocus after shooting – a very neat trick made possible by AI depth mapping and likely a sign of future “computational refocusing” technology.
Google’s Portraits: Pixels have a strong reputation for portraits as well. The Pixel 8 Pro uses its main 50MP and telephoto 48MP to gather depth info (and the ultra-wide for additional depth in some cases). Even with a single lens, Google’s dual-pixel tech provides two viewpoints from the split pixels on the image sensor, effectively like having a tiny stereoscope for every shot. The Pixel’s AI then creates a depth map, and, importantly, Google has tuned its bokeh shape and fall-off to look like a high-quality camera. The Pixel’s portrait mode usually uses the telephoto (5x on Pixel 8 Pro, or a cropped sensor if closer) to give a more flattering perspective. Real Tone algorithms ensure the skin colors are accurate and pleasing for all skin tones, a point Google emphasizes in its AI pipeline. Reviewers often praise Pixel portraits for their contrast and detail. One advantage Google introduced is Portrait Light (on older Pixels) where you could adjust lighting on faces after the shot – an AI feature not unlike Apple’s Portrait Lighting. New on the Pixel 8/8 Pro, thanks to Tensor G3, are features like Best Take – which is more for group photos (letting you swap faces from a burst to get everyone looking their best) – and Face Unblur, which actually uses a secondary camera to grab a sharper image of a face if it was moving, then blends it in. dxomark.com ↗️ These are tangential to portraits but show how AI is used to ensure the subject (often a person) looks good. In terms of pure subject isolation, the Pixel’s portrait segmentation is excellent but can occasionally trip up on fine details – DXOMARK noted segmentation errors in bokeh mode as a con for the Pixel 8 Pro. The ability of the Pixel to do portraits in Night Sight (Night Sight Portrait) also shows AI’s power: it merges long exposures but keeps the subject sharp, applying the blur only after ensuring the subject is well-exposed and delineated. blog.google ↗️ Overall, Pixel portraits are characterized by a pleasing depth effect and bold processing (sometimes a bit high in contrast compared to the softer Apple look). With Pixel 8 Pro’s improved sensors, detail on faces is superb and the bokeh looks even more convincing now.
Samsung’s Portraits: Samsung’s high-end phones typically let you choose 1x or 3x for portraits (the S24 Ultra has both a standard and a tele lens for this purpose). Using the 3x telephoto for portraits gives a nice optical background blur in addition to the artificial blur – often yielding a very DSLR-like look for half-body shots. Samsung’s AI portrait segmentation has improved, but in the past it occasionally struggled with hair or complex backgrounds. The S24 Ultra benefits from the new AI features in its camera system – what Samsung calls the Pro Visual engine includes Reflection Remover and other enhancements. techradar.com ↗️ Reflection Removal is great for shooting through glass (it can AI-edit out window glare), but for portraits specifically, Samsung provides effects like Studio, High-key mono, etc., similar to Apple. The background blur intensity can be adjusted in Samsung’s portrait mode after the fact as well. With the large main sensor and dual telephotos, the S24 Ultra has plenty of depth data. Its portraits in bright light are praised – DXOMARK mentioned the S24 Ultra’s portrait shots in bright light had “vivid and natural color” that sometimes looked even more realistic than the iPhone’s in their tests. dxomark.com ↗️ That’s a notable point: Samsung seems to have dialed back the overly smooth “beautification” that it was once known for, now aiming for realism like Apple/Google in rendering faces. In lower light, Samsung’s portraits can get softer, but the phone will smartly auto-switch to Night Portrait when needed, and use AI to boost face brightness. Samsung also allows portrait video (aka Live Focus Video) which blurs the background in real time – this uses AI on the Snapdragon ISP to segment persons in motion. While fun, it’s not always perfect at hair edges when moving. For still portraits, expect the Galaxy S24 Ultra to deliver excellent results – perhaps with a tad more saturation in colors and slightly less fine detail on faces compared to the iPhone/Pixel (due to stronger noise reduction or smoothing). But these differences are small. All three phones can produce stunning professional-looking portraits with creamy bokeh, all thanks to AI depth estimation algorithms that have been trained on countless images of people.
One more aspect worth noting is autofocus and subject tracking – it’s indirectly AI. Apple and Samsung use face detection and even eye tracking to focus for portraits, ensuring the eyes are sharp. Google’s camera app will automatically lock focus on people’s faces too. These devices also use AI to choose the best shot in burst – e.g., Apple’s Live Photos and Google’s Top Shot can pick a frame where the subject isn’t blinking or blurry. In summary, shooting a portrait on a flagship phone now invokes a complex AI pipeline: detect subject -> measure/estimate depth -> segment subject from background -> apply a realistic blur gradient -> enhance the subject’s details. The user just sees a nice portrait; under the hood, AI is the portrait artist.
Super-Resolution Zoom: Pushing Beyond Optical Limits
Zoom has become a major battleground for smartphone cameras, especially as manufacturers incorporate high-megapixel sensors and periscope telephoto lenses. AI plays a crucial role in enhancing zoomed images, both by stabilizing and merging multiple frames for detail (super-resolution) and by upscaling intelligently beyond the optical range. The latest flagships each have impressive zoom capabilities that rely on computation as much as optics.
Optical hardware overview: The iPhone 15 Pro Max introduced Apple’s first periscope telephoto lens, achieving 5× optical zoom (120mm equivalent). The Pixel 8 Pro has a 5× telephoto (approximately 120mm as well) at 48MP. Samsung’s Galaxy S24 Ultra actually has two dedicated zoom lenses: a mid-range 3× (10MP) and a periscope ~5× (50MP sensor) that can go up to 10× digitally cropped with high quality, plus the ability to do crazy 100× digital zoom. These hardware choices set the stage, but beyond the optical 5×, all zoom performance depends on computational “super-resolution” algorithms.
Google’s Super Res Zoom: Google has been a pioneer here. Starting with Pixel 3, they introduced Super Res Zoom, an AI-enhanced digital zoom technique. The concept is clever: when you zoom beyond the optical capability, the Pixel leverages the natural hand tremor and captures multiple frames, each slightly offset. By combining these multiple frames, the system can reconstruct detail at a higher resolution than a single shot could. androidauthority.com ↗️ Essentially, it uses multi-frame super-resolution – aligning features across frames to increase clarity. Machine learning comes in to pick the “best bits” of each frame and to interpolate missing detail. On the Pixel 8 Pro, Google claims up to 30× zoom with usable results, far beyond the 5× optical. As Android Authority explains, Super Res Zoom is an AI-powered feature that improves quality on digitally zoomed images, avoiding the typical blur of simple cropping. androidauthority.com ↗️ It works best when you have slight movement (for alignment data) and plenty of light. With Tensor G3, the Pixel 8 Pro can even apply some of this to video and to post-shot cropping (the upcoming Zoom Enhance feature in Google Photos will let you crop in on a taken photo and then upscale it using the stored high-res data and AI. gsmarena.com ↗️ Practically, Pixel’s Super Res Zoom means that at intermediate zoom levels (like 2×, 3×), it doesn’t need a dedicated lens – it uses the 50MP main sensor and super-res algorithms to produce a sharp image that looks optical. At high zoom (10×, 20×, etc.), it combines telephoto lens data with multi-frame processing. The results on Pixel phones have been impressive: you can often get a 10× shot with surprising detail for a phone. Where AI really helps is denoising and deblurring the digital zoom. By learning from patterns, the Pixel’s algorithms can fill in edges (for example, text on a distant sign might be more legible with super-res than a basic crop). The limits do show up at the extreme – 30× is usually only for identification, not for printing, as artifacts will appear. But it’s far better than nothing. Google continuously improves this: the Pixel 8 Pro’s 48MP telephoto ensures that even 5× is very sharp, and beyond that the phone leverages both the tele and main sensor together (for instance, around 10× it might fuse data from both). DXOMARK commented that the Pixel 8 Pro had “good detail across all zoom settings” up to its max, and a well-balanced zoom performance without major jumps. dxomark.com ↗️ This consistency is a testament to Google’s seamless handoff between optical and AI zoom.
Apple’s approach to zoom: Apple was a bit late to the super-zoom game, sticking with 2× and 3× lenses for years. With the iPhone 15 Pro Max, they now have a 5× optical (using an innovative tetraprism design). Apple’s strategy uses the high-resolution sensors to advantage. The 48MP main camera on recent iPhones allows a 2× lossless digital zoom by cropping the center 12MP – effectively functioning as a 2× lens without quality loss. apple.com ↗️ Beyond that (and beyond the 5× tele), Apple does digital zoom up to 25× on the 15 Pro Max. To maintain quality, Apple employs its Photonic Engine with techniques similar to Deep Fusion on zoomed shots. For example, at intermediate zoom levels (like 10×, which is beyond the optical 5×), the iPhone likely uses a mix of the 5× tele and data from the 48MP main to add detail (if the scene is well-lit). It will also run multi-frame merges – taking several shots quickly and fusing them to reduce noise in the digitally zoomed result. We’ve seen Apple’s approach yield decent 10× images, but generally Apple has been more conservative with extreme zoom. The emphasis is on optical quality for the primary ranges (0.5× ultra-wide, 1×, 2×, 5×). Still, in good light, an iPhone at 10× or 15× can be surprisingly usable thanks to AI upscaling – it just doesn’t advertise it explicitly as Google does. What Apple does tout is how on-sensor crop plus AI gives a clean 2×: that’s essentially Apple’s form of super-resolution using the high-res sensor (and presumably combining multiple 48MP frames to generate a sharp 12MP 2× photo). apple.com ↗️ In low light, the Photonic Engine will intelligently decide whether to use the telephoto lens or crop the main (whichever yields better detail and less noise). Apple’s philosophy often is to err on the side of less zoom if it means a better image, whereas Samsung will zoom further but with more aggressive processing to compensate. With the iPhone 15 Pro Max now second in DXOMARK’s “Zoom” category among these three, it shows Apple caught up by adding the 5× lens. dxomark.com ↗️ But it still trails some competitors in extreme zoom capability (no match for Samsung’s 10× optical or 100× digital reach). In practice, up to 5× the iPhone is excellent; at 10×, it’s okay for casual use; beyond that, detail drops notably. We might expect Apple to use even larger sensors or higher megapixels in future to further leverage AI for zoom.
Samsung’s Space Zoom and AI Upscaling: Samsung leads in raw zoom numbers – the Galaxy S24 Ultra offers “100× Space Zoom”, a feature that combines its 10× optical capability (S24 Ultra actually doesn’t have a separate 10× lens this year, instead it uses a 5× 50MP and crops to achieve 10× effectively. techradar.com ↗️) with heavy AI upscaling. Samsung’s zoom pipeline is very sophisticated: at high zoom levels, the camera app will often take multiple frames (even a short video snippet) and use optical stabilization plus software alignment to mitigate hand shake. It then feeds those frames into an AI zoom algorithm that enhances detail. They mention “AI-powered 100x Space Zoom [that] intelligently processes information to capture clearer, brighter pictures without compromising quality”. samsung.com ↗️ In plain terms, when you zoom in on something far away (say a distant sign or the moon), the phone is using trained models to sharpen edges and sometimes even recognize patterns (controversially, earlier Samsung models would aggressively optimize moon photos by essentially matching them to known moon textures – an example of AI generation). For typical scenes, though, the AI zoom tries to reconstruct fine details (like text or facial features) that get lost when you zoom. The Galaxy S24 Ultra’s two tele lenses (2×/3× and 5×) cover a range of focal lengths natively, and Samsung’s camera app will switch between them (or combine them) as you pinch zoom. There is a certain range where it blends sensor data for the best result. Super Resolution processing is definitely at work: at mid-zooms the 200MP main sensor might contribute data. For example, around 4×, Samsung might crop the 200MP sensor instead of using the 10MP 3× lens, if it calculates it can get a sharper shot that way, using multi-frame super-res from the main sensor’s detail. At 30× or 50×, it will use the 5× periscope and then apply super-res stacking to clarify the image. Users often find Samsung’s 30× shots quite usable (though a bit over-sharpened sometimes), and even 100× can serve to identify a distant object or read a far-away sign that would be impossible otherwise. The trade-off is that at very high zoom, you’ll see some processing artifacts – kind of a watercolor effect or ringing from over-sharpening. Samsung’s processing tends to favor a crisp look, so it may aggressively sharpen edges that the AI thinks should be there. Sometimes this guesswork can misfire (yielding fake-looking details), but often it gives the impression of a clearer photo from far away. The Galaxy S24 Ultra’s Snapdragon 8 Gen 3 ISP is surely leveraging those real-time semantic segmentation skills as well – it can perhaps treat text differently from natural textures, etc., when upscaling. In marketing, Samsung points out you can now “capture the action from afar without sacrificing detail or clarity” and cites the AI zoom processing on the S24 Ultra as a key to that. samsung.com ↗️
Comparison and real-world results: In general, Apple and Google limit digital zoom to ~25-30×, whereas Samsung pushes to 100×. At moderate zooms (say up to 10×), all three do quite well, using a mix of optics and AI. The Pixel 8 Pro perhaps has the edge in consistency at those levels – its super-res algorithm yields consistently sharp results up to 10× without needing a second tele lens for mid-range. dxomark.com ↗️ The iPhone 15 Pro Max produces excellent detail up to 5× optically; between 5× and 10× it still competes but with a bit less detail than Pixel or Samsung (as seen in some reviews where at 10×, Samsung’s image, aided by a higher native zoom, was crisper). The Galaxy S24 Ultra shines when you need extreme reach – e.g. 30× to 100×. It can capture things no other phone can, though the quality will be more like a sketch of reality than a clean photo. A TechRadar shootout of the 15 Pro Max, S24 Ultra, and Pixel 8 Pro found that at 5× all were excellent, at 10× the Samsung had an advantage in detail (due to its tele lens and processing), and by 20-30× the Samsung was really the only one still producing a recognizable image, thanks to its computational “Space Zoom” tricks, whereas the iPhone maxes out by 25× and Pixel by 30× (with heavy degradation). techradar.com ↗️ DXOMARK’s zoom sub-scores also reflect these strengths: the Pixel 8 Pro and iPhone 15 Pro Max scored very well for short-range and mid-range zoom, but Samsung leads in long-range zoom (even if the overall DXOMARK “Zoom” score wasn’t #1, likely because of some noise/artifacts considerations). dxomark.com ↗️
Another aspect is zoom in video – which is more challenging. Google actually introduced a form of Super Res Zoom for video on the Pixel 9 series (allowing high zoom in video with ML frame stitching. yahoo.com ↗️), but on Pixel 8 Pro it’s not as advanced (though you can zoom and the Tensor chip will use some stabilization and denoise). Apple’s video zoom is limited to the optical range for best results; digitally zooming in video beyond that can get noisy, though the A17 Pro chip will still apply some smart processing. Samsung’s video can actually do 10× and even 20× with decent results in good light, using the tele lenses and live processing (but again, quality drops at extreme zoom).
In summary, AI-driven super-resolution has extended smartphone zoom to new heights. By fusing multiple frames and using trained models to enhance detail, phones are overcoming the limitations of tiny lenses. Google’s Super Res Zoom is a standout implementation for stills, Apple uses its big sensors and Photonic Engine for solid zoom quality without boastful claims, and Samsung pushes the envelope with the longest reach and increasingly clever AI enhancement to make those long zoom shots usable. The user benefits by being able to optically “get closer” to subjects without moving, whether it’s capturing wildlife, sports, or just details on a building across the street – something that in the past was impossible on a phone.
Video Enhancements and AI-Powered Video Modes
Stills aren’t the only place where AI is changing the game – video recording on smartphones also sees big benefits from computational photography and AI. Modern flagships use AI for everything from stabilization to HDR processing in each frame, and even for special effects like background blur in video or noise reduction in low light clips.
Real-time HDR and color processing: The iPhone has long been a leader in video, and one reason is Apple’s ability to do HDR video (Dolby Vision) on the fly. The iPhone 15 Pro Max can capture 10-bit Dolby Vision HDR at up to 4K 60fps, meaning each frame is processed for optimal tone mapping, with the Neural Engine ensuring accurate colors and dynamic range. Apple’s ISP and Neural Engine work together to apply Smart HDR principles to video – adjusting exposure and contrast per frame, and even using segmentation to handle skies vs. faces in video. The result is industry-leading video quality; DXOMARK ranks the iPhone 15 Pro Max as the top in video, “standard-setting” in many aspects. In fact, DXOMARK noted the S24 Ultra’s video, while improved in color, still “lagged significantly behind the class-leading Apple iPhone 15 Pro Max” in exposure and color stability. This shows Apple’s advantage in consistent processing – likely due to very well-tuned AI algorithms that maintain image quality across changing conditions (e.g., as you pan from a dark area to a bright area, the iPhone adjusts smoothly without pumping exposure). Samsung’s S24 Ultra can also record HDR10+ video, and it uses Super HDR video processing to get good dynamic range, but some reviews point out it can have exposure or white balance shifts in video. Google’s Tensor-powered Pixels have something called HDRnet – a real-time neural network that applies HDR processing to each frame of video with low latency. This was introduced with Pixel 4 for video and continues – it ensures Pixel videos have nice dynamic range without blown highlights. The Pixel 8 Pro also records in 10-bit HDR. So all three are applying HDR principles to video, but Apple’s consistency stands out.
Stabilization and Action modes: AI plays a huge role in video stabilization. While optical stabilization hardware helps, phones apply electronic stabilization by cropping and tracking motion via gyroscope and scene analysis. Apple’s Action Mode (introduced on iPhone 14) is a prime example – it uses a large crop from the 4K sensor and advanced algorithms to smooth out extreme shakes (like a GoPro). The Neural Engine likely helps predict and correct motion between frames. Samsung has a similar Super Steady mode (toggling to the ultra-wide camera and heavily stabilizing to mimic an action camera), and Google also has stabilization modes (Standard, Active, and Locked for far zoom) that use AI to reduce tremors or even lock onto a subject (the Locked mode on Pixel will keep a distant object centered by compensating for hand movements). These features all rely on analyzing many frames: essentially, the phone “looks” at video frames and figures out how to shift them so the end result is smooth. AI comes in to ensure this doesn’t introduce weird warps – identifying the background vs foreground motion, etc.
Portrait video (cinematic mode): One of the flashy AI video features is bokeh in video. Apple’s Cinematic Mode (available on recent iPhones) records video with a fake shallow depth of field, and even allows shifting focus between subjects with a tap. Achieving this requires each frame to have a depth map, so Apple’s Neural Engine does real-time segmentation of people and computes a depth effect using either stereo vision (dual camera) or machine learning estimates. It’s essentially Portrait mode at 30 frames per second. While quality isn’t as good as real optical bokeh (you may see occasional edge artifacts, especially when hair or complex shapes are present), it’s a remarkable demonstration of AI performance – the iPhone even intelligently racks focus if someone in the background “looks” towards the camera, mimicking a videographer’s behavior. Google added a similar Cinematic Blur video mode starting Pixel 7, also using AI for depth; it’s decent but also not flawless. Samsung had Live Focus Video which is similar – fun to use, but sometimes struggles with segmentation accuracy in motion. These modes will improve as AI models get better and chipsets more powerful.
Low-light video and denoising: Video in low light is tough, because the shutter speed per frame is limited. Here, phones use temporal noise reduction – combining information across multiple frames. AI can aid in detecting noise vs actual detail between frames. Google took a unique step with the Pixel 8 Pro by introducing Video Boost with Night Sight video, which actually offloads heavy processing to the cloud. When enabled, the Pixel 8 Pro records video and gives you a standard quality clip immediately, but meanwhile uploads the data to Google’s servers which then perform intense frame-by-frame processing (using similar algorithms to Night Sight for stills) on the entire video. A few hours later, you get back an upgraded video with cleaner shadows, higher dynamic range, and Night Sight-level brightness. This is basically cloud AI doing what the mobile chip cannot yet do in real-time for video. It’s an opt-in feature and shows how demanding true Night mode video is. Apple and Samsung haven’t gone the cloud route; instead they rely on ISP improvements. The Apple A17 Pro and Samsung’s Snapdragon Gen 3 both claim better low-light video. Indeed, the iPhone 15 Pro’s low-light video is improved, with less noise – Apple cites improvements in low-light video and Action mode thanks to the new ISP. Samsung’s S24 Ultra still shows noise in very dark video scenes according to DXO (that was a con mentioned: noise in low-light video). This indicates that while photos can be almost night-vision-like, video is still catching up. But AI helps by doing smart noise reduction (filtering noise while preserving moving subjects) and adapting frame exposure on the fly.
Other AI video tricks: Audio is sometimes overlooked, but Google added an Audio Magic Eraser that uses AI to reduce unwanted sounds in your video (e.g. traffic noise) – a post-processing feature that showcases AI beyond visuals. Samsung’s phones have AI HDR playback (tone mapping videos for their displays) and even AI generated subtitles in real-time translation (a Galaxy AI feature, though not directly camera-related). There are also niche features like Samsung’s Director’s View which uses AI to let you record from multiple cameras at once and even remove the background behind the person in the front camera (Qualcomm showcased this, where the person’s background is cut out and overlaid on the rear camera view – essentially a live green screen effect). These are fun examples of what’s possible with AI and powerful mobile chips.
In summary, AI’s influence on video is about making your footage look more professional and polished automatically. From ensuring focus and exposure are spot-on in varying conditions, to smoothing out your jogging footage, to enabling creative effects like background blur or even night vision video via cloud AI – the latest phones leverage a combination of hardware (ISP, gyro, multi-camera) and software (neural nets, computer vision) to elevate mobile videography. It’s no surprise that the iPhone – with Apple’s tight integration of hardware/software – leads in video, but Google is innovating with cloud assist, and Samsung isn’t far behind, offering the most flexibility (even 8K recording with decent stabilization, which is a lot of data to crunch).
All these AI enhancements mean that average users can get shake-free, well-exposed video of their life events without complicated setups – just point and shoot, and let the phone handle the rest.
Comparing the Flagships: AI Strategy and Results
Having looked at specific features, it’s worth comparing Apple, Google, and Samsung’s overall AI camera strategies side by side:
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Apple focuses on a tight integration of hardware and software, using AI mostly behind the scenes. They don’t shout “AI” in marketing, but features like Photonic Engine, Deep Fusion, and Smart HDR are entirely AI-driven under the hood. Apple leverages its powerful A-series chips (the A17 Pro in iPhone 15 Pro) to run complex neural models for segmentation, tone mapping, and fusion on-device in real time. The result is an “it just works” experience – minimal toggles (the camera app is simple) yet consistently excellent output. Apple’s philosophy is to preserve a natural look: photos with true-to-life colors and balanced processing, and industry-leading video. In comparisons, the iPhone often nails exposure and skin tones and has the most reliable performance across scenarios. Even if a Pixel or Samsung might win a specific category (say extreme HDR or extreme zoom), the iPhone is almost always near the top in every category. This consistency is arguably due to Apple’s careful tuning of AI – they avoid pushing any single metric too far at the expense of others. Additionally, Apple’s approach to user features (like the automatic capture of depth for later portrait effect, or the seamless switch to macro mode with the ultra-wide lens) shows an AI strategy centered on enhancing user convenience. They also prioritize on-device processing (no cloud required) to maintain speed and privacy. Apple’s camera pipeline is somewhat conservative in offering manual controls – you trust its AI to do the right thing, and usually it does. techradar.com ↗️
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Google takes a more AI-centric approach in features and marketing. The Pixel’s entire camera experience is intertwined with Google’s computational photography heritage and its new Tensor chip designed specifically for machine learning tasks. Google isn’t afraid to introduce novel AI features: Magic Eraser to remove unwanted objects from photos, Best Take to merge group shots, Photo Unblur to sharpen old images, and even the ambitious cloud Video Boost for Night Sight video. Many of these go beyond capture into post-processing magic, reflecting Google’s AI software prowess. In terms of image results, Pixel cameras are known for their dramatic HDR, excellent Night Sight, and consistently good results with minimal effort. The Pixel tuning can be a bit stylistic – some users love the contrasty look and punchy colors (and indeed Pixel phones often win blind photo comparison tests where average people choose the most eye-catching image). Others might find them slightly less natural than iPhone. Nonetheless, the Pixel 8 Pro is among the top-ranked camera phones, delivering pleasant colors (especially skin tones via Real Tone) and accurate exposure in virtually all conditions. Google’s AI strategy is also about extending what’s possible: e.g., astrophotography mode to capture stars (a niche but amazing capability), or using AI to upscale zoom up to 30×, or leveraging the cloud to process video frames that a mobile chipset can’t handle in real time. One could say Google leans more on AI to overcome hardware limits – the Pixel 8 Pro’s camera hardware is very good, but not as extravagantly specced as Samsung’s (no 200MP sensor or dual tele), yet through clever algorithms it competes at the top. Google also aims for user-friendly AI – Magic Editor in Google Photos is a clear example, where the user can easily manipulate images in ways that would have required Photoshop skills. All this shows Google’s vision of computational photography as a holistic package of capture and editing, powered by AI. dxomark.com ↗️
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Samsung tends to go for the “specs + AI” combo – they provide cutting-edge hardware (multiple cameras, high resolution sensors, etc.) and then use AI to maximize what that hardware can do. The Galaxy S24 Ultra’s camera system is overflowing with features: from Nightography to Space Zoom to generative image editing. Samsung’s Galaxy AI features are built into the camera app and gallery. For example, the S24 Ultra has an AI Photo Remaster that can brighten up a dark photo or increase resolution, an Object Eraser similar to Magic Eraser, and new Generative Fill that can expand an image’s background using AI (one UI feature listed in the TechRadar spec table). These show Samsung integrating AI functionalities to keep up with (or copy) Google’s ideas. In terms of capture, Samsung uses AI for scene optimization – e.g., automatically detecting what you’re shooting (food, landscape, night scene, etc.) and adjusting settings. Sometimes this can lead to the infamous “Samsung look” – vibrant, sharpened images that wow on social media. Interestingly, the S24 Ultra’s tuning seems to be shifting slightly towards realism, as evidenced by those DXOMARK comments praising its natural color in some shots compared to prior generations. Samsung’s heavy AI lifting is apparent in extreme scenarios: hand-holding 30× zoom, or shooting portraits in the dark, or recording very steady hyperlapse video – these all rely on algorithms beyond basic camera capture. Samsung’s strategy is basically leverage brute-force hardware and refine with AI. It may not always be as subtle as Apple or Google’s approach, but it’s incredibly effective. The user gets a versatile camera that can do a bit of everything – you have more lenses to play with, more modes and toggles (Pro mode, Single Take, etc.), and the phone will use AI to glue it all together (like automatically merging data from two telephoto lenses to cover the zoom range, or using AI segmentation to improve HDR as mentioned). One downside is that Samsung’s camera app and processing pipeline are very feature-rich, which at times can result in slightly slower shot-to-shot time or occasional quirks (e.g., the delay noted between pressing the shutter and capture in some conditions). But with the Snapdragon 8 Gen 3, performance has improved. techradar.com ↗️
Benchmark and review consensus: All three of these flagships sit at or near the top of most camera rankings. DXOMARK’s scores (as of early 2024) placed the iPhone 15 Pro Max and Google Pixel 8 Pro very close, both among the top five globally, with the Pixel slightly ahead in still photo sub-score and the iPhone leading in video. The Galaxy S24 Ultra was a bit behind those two in overall rank (around 18th according to one Samsung community reference, due in part to its video and artifact scores), but it’s still one of the best Android cameras especially for versatility. In real-world usage, you could give any of these phones to a casual photographer and they’d be thrilled with the results. The differences are nuanced: Pixel might capture the scene with a bit more drama, iPhone with more fidelity, Samsung with more brightness and detail in certain aspects. It’s telling that in a TechRadar head-to-head, there was no absolute “winner” – instead, each phone won in different categories, and the author concluded they all have strengths (the Pixel was lauded for HDR and low light, iPhone for video and consistent output, Samsung for zoom and vibrant detail). For users, it might come down to preference: do you prefer the Pixel’s computational prowess, the iPhone’s balanced pro-grade output, or Samsung’s all-in-one versatility? dxomark.com ↗️
One should also note the future trends hinted by these strategies: Google leaning into cloud AI suggests future phones might do hybrid processing (device + cloud) for even more complex tasks. Apple’s focus on on-device neural processing indicates they’ll keep beefing up chip AI performance (possibly for things like real-time scene reconstruction or even 3D capture). Samsung’s partnership with Qualcomm means we’ll see more integration of Snapdragon’s generative AI capabilities (the Gen 3 chip can run things like stable diffusion image generation on-device, and who knows – maybe you’ll be able to tell your phone “make this photo’s sky sunset instead of noon” and it will). We already see a touch of that with Samsung’s generative fill. theverge.com ↗️
Conclusion: How AI is Shaping the Future of Mobile Photography
The current landscape of smartphone cameras demonstrates that AI-driven algorithms are just as important as sensors and lenses in creating a great photo. The iPhone 15 Pro Max, Google Pixel 8 Pro, and Samsung Galaxy S24 Ultra each showcase a different strength of computational photography: Apple with its seamless blending of neural processing for realistic results, Google with its audacious use of AI to push boundaries (and even correct your photos after the fact), and Samsung with its fusion of top-notch hardware and feature-packed AI enhancements.
User-visible results have improved dramatically because of these AI techniques. We now expect our phones to produce vibrant HDR photos with no blown highlights, clear night images where we can actually see the scene, portrait shots that rival professional cameras, and stable, high-quality video. And largely, these devices deliver on those expectations. Much of this is thanks to advancements like multi-frame fusion, semantic segmentation, and neural network-based image processing – all executed in a fraction of a second by the specialized AI cores in our phones.
Looking ahead, the trends suggest even more AI integration. We can anticipate smarter scene understanding (for example, the camera knowing exactly what you’re shooting and adjusting style – maybe recognizing a specific landmark and adjusting for haze automatically or framing it optimally), more AI-assisted creativity (perhaps the ability to re-light photos or change backgrounds flawlessly), and continued improvement in core image quality via machine learning (better demosaicing, better autofocus tracking through AI, etc.). Computational photography is also bridging into the realm of augmented reality – the same depth maps and segmentation used for portraits are laying groundwork for AR content, 3D photo capture, and more.
Importantly, AI is making high-level photography accessible to everyday users. You no longer need to understand exposure bracketing or carry a tripod for night shots – the phone, with its “software defined camera”, handles it. In the U.S. and globally, this has made smartphones the primary camera for most people, and it’s why companies invest heavily in these AI camera systems. Flagship phones will likely continue one-upping each other with new AI features (like Google’s Pixel 8 series adding best take and audio magic eraser, Samsung likely to respond with similar or new tricks, Apple possibly introducing its own spin on AI photo editing in the future). The competition benefits consumers as we get sharper, more beautiful memories captured with minimal effort.
In 2024 and 2025, algorithms are truly driving mobile photography – in many ways, the camera experience is defined by software. As we’ve seen, Apple’s Photonic Engine, Google’s Tensor-powered computational wizardry, and Samsung’s Galaxy AI engine all aim to transcend the limitations of small phone cameras, each in their own way. And they have succeeded – the best camera phone is no longer just about the biggest sensor or lens, but about the smartest combination of silicon and code. This synergy of optics and AI will only grow stronger. For consumers, that means we can look forward to even more amazing photos and videos from devices that fit in our pockets, as the line between computational photography and traditional photography continues to blur (quite literally, in Portrait mode!).
In conclusion, the current flagship smartphones demonstrate that AI is not just an add-on to photography, but its driving engine. From the instant we press the shutter to the final image we see, countless AI decisions and computations ensure that moment is captured in the best possible way. As technology marches on, mobile photography will become even more intelligent – and given the remarkable achievements by 2025, the future of smartphone cameras is incredibly exciting, with algorithms leading the way to images that are more breathtaking and true to our vision than ever before.
