ZDNET’s Kerry Wan recently captured a photograph with the cutting‑edge Google Pixel 10 Pro, showcasing the remarkable advancements embedded within its latest camera system. This device is more than just another iteration in the Pixel lineup—it represents the culmination of years of refinement, innovation, and relentless pursuit of excellence by Google’s Pixel Camera team.
One key figure behind this evolution is Isaac Reynolds, who has been part of the Pixel Camera project since the very first Pixel phone debuted in 2016. Over nearly a decade of involvement, Reynolds has witnessed a succession of technological milestones, yet he insists that his optimism and enthusiasm for this year’s Pixel 10 Pro are unparalleled. His confidence derives from a fresh wave of artificial intelligence breakthroughs—particularly in large language models, generative imaging, and advanced machine learning techniques—that have collectively empowered Google to make smartphone photography far more versatile and intelligent than ever before.
In a conversation with Reynolds, held shortly after the whirlwind launch of the Pixel 10, his excitement was evident. He was simultaneously reflecting on the successes of the current generation and preparing for the ambitious innovations projected for the 2026 Pixel lineup. During this exchange, our discussion traversed numerous areas: from practical features such as Pro Res Zoom, Conversational Editing, and Camera Coach, to deeper behind‑the‑scenes innovations like Telephoto Panoramas, Guided Frame, Auto Best Take, and the metadata standards Google is adopting to ensure transparency in AI‑powered imagery.
Central to Reynolds’ philosophy is a focus on what he calls “durable photography problems.” These are long‑standing and universal challenges such as capturing sufficient detail in low‑light conditions, preserving dynamic range, achieving effective digital zoom, and recovering clarity in complex textures. Each new Pixel generation, he explained, attempts to resolve some part of these enduring problems with meaningful technological leaps.
One illustrative solution is the feature known as Camera Coach. Leveraging the broad contextual understanding of large language models, Camera Coach can actually guide the photographer in ways traditional technology cannot. For instance, the system may instruct a user to shift their perspective, lower the viewpoint, or rotate the framing—physical adjustments that hardware alone cannot accomplish. With such interactive coaching, Google’s camera assists users beyond computational tricks by teaching them how to naturally capture better results.
Similarly transformative is Conversational Editing, technically housed within the Google Photos application but keenly integrated into the Pixel ecosystem. This innovation allows a user to describe, through voice or text, the alterations they wish to make in a photograph—such as removing an unwanted object, adjusting colors to match personal memory, or drawing more focus to the subject in the center. The AI then seamlessly executes these commands. Reynolds emphasized that the feature eliminates the clunky, manual editing interface and replaces it with a direct dialogue between the user and the machine. In practical use, it might suggest edits, like removing bystanders, and even presents these options in intuitive “suggestion chips.” This not only exemplifies AI as an assistant capable of completing tasks rather than simply presenting options, but also illustrates how quickly the technology has matured.
For professional photographers and enthusiasts alike, Pro Res Zoom emerged as a focal point of discussion. Long‑distance zoom has often required reliance on dedicated cameras with specialized telephoto lenses. The Pixel 10 Pro, however, introduces a generative AI approach that fills in missing detail left behind by digital zoom, reconstructing images with intelligence rather than simple pixel interpolation. Reynolds recounted the progression of Google’s zoom technology—beginning with rudimentary interpolation, progressing through multi‑frame noise reduction, and culminating in the sophisticated pixel‑level probabilistic models used today. The result is an upscaling process capable of recognizing structures and surfaces, such as brickwork or foliage, and rendering them with realism rather than blending colors into uninspired averages. The one caveat: when human faces are detected, the system deliberately avoids generative reconstruction in order to preserve authentic identity, respecting the heightened sensitivity humans have for facial features.
As AI assists more actively in image construction, Google has also given serious thought to authenticity and transparency. With its participation in the Coalition for Content Provenance and Authenticity (C2PA), and through tools like DeepMind’s SynthID watermarking, Google now embeds metadata to indicate whether AI influenced the image. Reynolds himself oversaw this delicate project, stressing how critical it is for users to understand what AI can or cannot do. This approach ensures that viewers down the line can make informed judgments about the reliability and origin of what they see.
Beyond optics and authenticity, education plays a central role. Reynolds underscored that public perception often lags dramatically behind technical reality. Demonstrating side‑by‑side comparisons—such as the difference between native zoom and AI‑enhanced zoom—helps build user trust. By labeling images with transparent credentials, users can evaluate authenticity themselves, thereby fostering confidence in these digital tools while giving them agency in deciding the role AI played in their photos.
Among additional subtle innovations are Telephoto Panoramas. Unlike traditional video‑based panorama stitching, which involves creating small slices of hundreds of frames and often leads to distortions, Google’s new method instead combines a handful of high‑quality still photographs. This method, enriched with the same computational processes that power Night Sight and HDR+, produces panoramas with immense resolution—up to 100 megapixels—while minimizing visual artifacts. Such high‑fidelity panoramas allow for cinematic representations of landscapes, whether stitched together using the 2x optical‑quality zoom or the Pixel Pro’s 5x telephoto lens.
The theme of inclusivity also permeates Google’s design strategy, with features like Guided Frame aimed at accessibility. For users who are blind or have limited vision, Guided Frame employs AI to describe what the camera sees and to provide feedback on composition, making it possible to take a well‑framed selfie or portrait even without being able to visually confirm the shot. Because visual communication remains a fundamental part of social interaction, this feature extends meaningful creative independence to communities often overlooked in mainstream smartphone design.
Meanwhile, Auto Best Take represents an evolution of Google’s previous group‑photo features. The system rapidly evaluates up to 150 frames captured in seconds, seeking the best possible alignment of open eyes, smiles, and attention toward the camera. Rather than forcing users to take multiple photos in hopes of securing one usable shot, the AI seamlessly analyzes, selects, or blends frames, ensuring the final product represents the ideal group moment. Reynolds explained that the ultimate goal is simplicity: the user presses the shutter once, and Google’s algorithms ensure the resulting photo is perfect, reducing frustration and wasted effort.
The powerful new Tensor G5 chip lies at the foundation of many of these abilities. Built using TSMC’s 3‑nanometer process, it marks a sharp leap from prior Samsung-manufactured processors. Reynolds described the performance gains as some of the most dramatic he has ever witnessed: operations that once took minutes now resolve within seconds. With a 60% boost in power to the Tensor Processing Unit, features like Pro Res Zoom can fully realize their potential without compromising usability.
This naturally led to an exploration of the AI models themselves. Contrary to the perception that Google relies on one singular, monolithic Gemini model, Reynolds clarified that the internal framework consists of numerous finely tuned and task‑specific models. For example, features such as Magic Eraser employ generative algorithms, but not necessarily Gemini itself. Each AI model in the Pixel pipeline is carefully crafted, optimized, and evaluated for the precise challenges it is meant to overcome, ensuring efficiency and reliability rather than a one‑size‑fits‑all approach.
At the end of these discussions, one conclusion becomes inescapable: Google stands alone among the small number of organizations shaping frontier AI while simultaneously producing its own hardware to embody those software advances. In the Pixel 10 Pro, this synergy between pioneering artificial intelligence and meticulously engineered smartphone design is no longer theoretical—it is tangible. The result is a device that not only addresses age‑old shortcomings in mobile photography but also lays the groundwork for an entirely new era where the camera does far more than capture—it intelligently interprets, enhances, and teaches.
Sourse: https://www.zdnet.com/article/how-pixel-10-pro-created-the-worlds-smartest-phone-camera-a-peek-inside-google/
