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Sony · Display · Filed Jan 28, 2025 · USPTO published Jun 18, 2026 · Jun 18 release

Sony Patents an Image Processor That Ignores the Parts of a Picture It Doesn’t Need

Most image processors crunch every pixel in a frame, whether it's relevant or not. Sony's new patent describes a chip that skips the parts it's been told to ignore — saving time and power in the process.

By · Updated · 3 min read

Sony Patent: Selective Image Processing With Flag Data, figure from US 2026/0170608 A1
Figure from the official USPTO publication.
Publication number
US 2026/0170608 A1(US20260170608A1)
Applicant
Sony Semiconductor Solutions Corporation
Filed · Published
Jan 28, 2025 · Jun 18, 2026
Inventors
Kohei Matsuda, Katsuhiko Hanzawa, Masato Motomura, Jaehoon Yu
Status when we published
Waiting for an examiner
Document
11 claims
US classification
382/100
Parent application
National Stage Entry of PCT/JP2023/024734 (filed Jul 4, 2023)(PCTJP2023024734)

What Sony's selective image processing actually does

Imagine a security camera scanning a parking lot. Most of the frame is empty pavement — but the chip still has to process every pixel just to confirm nothing interesting is happening. That's wasted work.

Sony's patent describes an image processor that uses a simple map — a list of flags marking which parts of a picture need processing and which parts can be skipped — to focus its number-crunching only where it counts. You define a target region, and the processor only runs its analysis there.

This kind of selective processing is especially useful in cameras embedded in cars, phones, or surveillance systems, where the processor has to make fast decisions without draining the battery or overheating. By ignoring irrelevant areas, the chip can work faster and more efficiently on the parts that actually matter.

How the flag-data system marks regions to skip

The patent describes two core components working together.

Region setting section: This part reads a piece of "flag data" — essentially a map layered over an image that marks each zone as either "process this" or "skip this." From that map, it defines an operation target region: the specific portion of the image the processor actually needs to work on.

Convolution operation section: Once the target region is defined, this unit runs a convolution operation (the standard math that neural networks use to detect edges, shapes, and objects in images) — but only within the flagged region. It uses the image data alongside a set of weighting coefficients (pre-trained numerical values that tell the system what to look for).

The upshot: instead of running the full computation across an entire image frame, the chip applies it selectively. The flag data acts like a stencil, directing compute resources precisely where they're needed. This architecture fits neatly into embedded vision systems — think automotive cameras, drone sensors, or smartphone camera modules — where processing speed and power draw are tightly constrained.

What this means for camera chips and real-time AI vision

Sony Semiconductor Solutions makes the image sensors inside a huge share of the world's smartphones, cars, and security cameras. A patent like this, focused on reducing wasted computation inside those sensors, is the kind of incremental efficiency work that compounds over time — faster on-device AI, longer battery life, or more accurate real-time detection without needing a more powerful (and expensive) chip.

For you as a consumer, the downstream effect could be a phone camera that processes scenes faster or a car's vision system that reacts more quickly — without the hardware getting bigger or hotter. It's not a flashy idea, but it's exactly the kind of focused optimization that separates good image chips from great ones.

Source: US 2026/0170608 A1 at the USPTO (PDF). Full patent text and figures are in the official publication.

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