Google Pixel 7 Pro rumours: Camera samples highlight phone's 30X Super Res Zoom
Official camera samples of the Google Pixel 7 Pro smartphone's 30X Super Res Zoom have surfaced online.
Google appears very confident about its machine-learning capabilities. In line with this, the American tech giant guarantees an exceptional super resolution zoom to the Pixel 7 Pro model's 30x zoom camera.
In other words, Google claims the Pro model can offer 10x more zoom than its predecessor, the Pixel 6 Pro. In fact, the company has now shared a series of camera samples that demonstrate the improvements.
Notably, the Pixel 7 Pro appears to retain the camera setup of the Pixel 6 Pro. However, Google has introduced some minor changes to the Pixel 7 Pro cameras.
Meanwhile, the search engine giant is drawing flak for offering a technically inferior zoom camera on the Pixel 7 Pro. The older Pixel 6 Pro comes with a better zoom camera than Google's latest flagship phone.
Now, the Pixel 6 Pro delivers 4x optical and 20x Super Res Zoom. However, Google claims the 7 Pro supports 30x Super Res Zoom and 5x optical zoom. This is a significant improvement in the camera department.
Interestingly, the Pixel 7 Pro uses enhanced machine learning rather than better optics to offer these advancements. Now, Google has demonstrated these improvements to Super Res Zoom through several camera samples.
The company captures a 12MP crop using the handset's 50MP main sensor and completes it with AI algorithms. For those unaware, Apple adopts the same method for the iPhone 14 Pro and Pro Max models.
The Dubbed Zoom Fusion technology reportedly delivers better results as compared to a classic digital zoom. Morever, Zoom Fusion integrates information from all three rear cameras together.
Regrettably, Google did not share similar digital zoom photos for comparison against the Super Rez images. Interestingly, the result seems to vary. For instance, the 30x zoom shot of the Golden Gate Bridge appears to be enhanced using AI.
The World Trade Center photos are clearer. Also, it is uncertain how well the new algorithms perform in low-light conditions.
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