The Google Pixel 10 and Pixel 11 may introduce significant upgrades in AI, and camera functionality, according to leaks from Google’s internal gChips division. These enhancements are driven largely by the Tensor G5 chip, anticipated to bring improved AI processing and new camera capabilities for the upcoming Pixel devices.
New AI-powered camera and editing features on Pixel 10:
According to the leak, the Pixel 10 series will leverage the Tensor G5’s enhanced TPU (Tensor Processing Unit) to expand Google’s AI-driven photo and video editing features.
One rumored feature, “Video Generative ML,” aims to simplify video editing by applying Generative AI directly within the Photos app, allowing users to perform intuitive post-capture edits. Additionally, Google may introduce “Speak-to-Tweak,” an AI-powered tool for voice-activated photo editing, and “Sketch-to-Image,” enabling users to turn basic sketches into complete images. These capabilities could give Google an edge in media editing, a central focus of its Pixel lineup.
Enhanced camera and low-light video capabilities on Pixel 11
The Pixel 11 is expected to elevate Google’s camera game with several new features. Machine learning will support up to 100x zoom for both photos and videos, made possible by a next-gen telephoto lens. This feature will allow for a versatile, AI-assisted zoom experience, although differences in quality between photo and video may persist.
Further advancements include a “Cinematic Blur” with reduced power consumption and “Ultra Low Light Video,” or “Night Sight Video,” which will enable clearer low-light recordings on-device. This mode should function optimally at extremely low light levels, comparable to dim candlelight, thanks to improvements in the camera hardware.
Tensor G5 could bring a major improvement in efficiency
Manufactured with TSMC’s 3nm process, it is expected to offer much-improved performance without excessive power drain, a critical factor when it comes to sustainability in resource-intensive tasks such as high-resolution video recording. This increase in power efficiency is also important for the on-device execution of AI-based features.
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