Video GOP Reorganization for Efficient Sparse Frame Decoding
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Solution Overview
Problem
Conventional sparse frame capture processing in video decoding results in inefficient decoding due to unnecessary decoding of many video frames.
Innovation Solution
A method involving the deletion of non-reference frames and extraction of instantaneous decoding refresh frames from groups of pictures (GOPs) based on attribute information, followed by selective sampling and decoding to obtain decoded frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform sampling of video frames is performed to obtain specific video frames and dependent frames, then sparse frame capture processing can be achieved, but many video frames are unnecessarily decoded resulting in low video decoding efficiency
Solution Approach 1:
The patent extracts and identifies reference frames (I-frames and P-frames) from the video stream using attribute information, then selectively decodes only these reference frames along with their dependent frames. Non-reference frames (B-frames) are excluded from decoding, thereby extracting only the necessary frames for sparse frame capture and eliminating unnecessary decoding operations.
Solution Approach 2:
The patent changes the decoding parameter by using attribute information (such as frame type indicators) to dynamically determine which frames require decoding. Instead of uniformly decoding all sampled frames, the system adjusts decoding behavior based on frame attributes, decoding reference frames and their dependents while skipping non-reference frames, thus improving decoding efficiency.
2Productivity
If non-reference frames are retained in GOPs for uniform sampling, then complete video frame sequences are available, but redundant frames increase decoding workload and reduce processing efficiency
Solution Approach 1:
The patent extracts reference frame information from attribute data associated with each video frame. By identifying which frames are reference frames (I-frames and P-frames) versus non-reference frames (B-frames), the system extracts only the necessary frames for decoding, removing redundant non-reference frames from the processing queue and reducing the total number of frames that need to be decoded.
Solution Approach 2:
The patent discards non-reference frames from the decoding process by identifying them through attribute information. While these frames are not decoded, their information is recovered or reconstructed through motion compensation from reference frames, allowing the system to eliminate redundant decoding operations while maintaining video quality and completeness.
Data Source
AI summary
A video processing method includes obtaining video frame attribute information, and first- and second-type groups of pictures (GOPs) from a video, deleting non-reference frame(s) in the first-type GOP and non-reference frame(s) in the second-type GOP based on the attribute information to obtain first- and second-type reorganized GOPs, extracting instantaneous decoding refresh frame(s) from the first-type reorganized GOP to obtain a target GOP not including the instantaneous decoding refresh frame(s), performing sampling on the target GOP and the second-type reorganized GOP in response to a quantity of the instantaneous decoding refresh frame(s) not meeting a decoding condition to obtain or more sampled frame(s), and performing video frame decoding on the sampled frame(s) and the instantaneous decoding refresh frame(s) to obtain decoded frame(s).