Sub-Image Matching in Video Encoding for Scrolling Screens
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Solution Overview
Problem
Video encoding in desktop scenarios, such as page scrolling and page flipping, is bandwidth-intensive and inefficient due to repeated encoding of similar image areas.
Innovation Solution
An image encoding method that partitions images into sub-images, classifies them based on feature values, and reuses encoding data for matched sub-images, while performing standard encoding on unmatched sub-images, reducing repeated encoding of valid pixels and optimizing bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video encoding reuses image encoding data from adjacent previous frames with same location and content, then encoding efficiency is improved, but in desktop scenarios such as page scrolling and page flipping, this approach becomes bandwidth-intensive and inefficient due to frequent changes
Solution Approach 1:
The image is divided into multiple sub-images (e.g., 8x8 or 16x16 pixel blocks), allowing the encoding system to process and compare only specific regions rather than entire frames. This segmentation enables selective reuse of encoding data for unchanged sub-images while encoding only the changed portions, thereby reducing bandwidth consumption in desktop scenarios where only parts of the screen change between frames.
Solution Approach 2:
The patent applies different encoding strategies to different regions of the image based on their change characteristics. Sub-images that remain unchanged between frames reuse encoding data from previous frames, while changed sub-images undergo full encoding. This localized approach optimizes the balance between encoding efficiency and bandwidth usage by adapting the encoding intensity to the actual content changes in each region.
2Reliability
If standard video encoding processes the entire current frame, then encoding completeness is ensured, but bandwidth consumption increases due to repeated encoding of unchanged image areas
Solution Approach 1:
The system performs preliminary comparison between sub-images in the current frame and corresponding sub-images in previous frames before encoding. By pre-identifying unchanged sub-images through feature value comparison (such as hash values or difference metrics), the system can skip encoding for these regions and directly reuse previous encoding data, thereby reducing bandwidth consumption while maintaining encoding completeness for all necessary regions.
Solution Approach 2:
For unchanged sub-images, the patent copies encoding data from previous frames instead of regenerating it. This copying approach preserves the complete encoding information for unchanged regions while avoiding redundant processing, thus reducing bandwidth consumption without compromising encoding completeness. The copied data includes all necessary encoding parameters and can be directly used to reconstruct the unchanged sub-images.
Data Source
AI summary
An image encoding method includes an encoding device that classifies sub-images of a to-be-encoded image based on feature values of the sub-images. Different sub-images have different feature values, and after matching the sub-images with encoded sub-images in a buffered queue, a matched sub-image and an unmatched sub-image may be determined. Therefore, image encoding data of a sub-image that is in a previous frame of image and that is the same as the matched sub-image in the to-be-encoded image can be reused only when the matched sub-image in the to-be-encoded image and the sub-image in the previous frame of image do not need to have a same location and same content. Further, in a process of encoding the to-be-encoded image, encoding (for example, standard encoding) is performed on the unmatched sub-image, and information about the matched encoded sub-image is generated for the matched sub-image.