Dynamic Bin Buffer Management for Video Entropy Coding
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Solution Overview
Problem
Existing video compression techniques face challenges in efficiently managing the bin-to-bit ratio during entropy encoding and decoding, leading to inefficient use of hardware resources and increased data volume due to the growing size, resolution, and frame rate of video data.
Innovation Solution
The method involves restricting the bin-to-bit ratio by variably setting thresholds for entropy encoding and decoding, and adaptively selecting encoding/decoding methods for each basic unit, using techniques like context-based adaptive binary arithmetic coding (CABAC) and exponential Golomb encoding to manage the bin buffer efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video data is compressed using existing entropy encoding methods, then data volume is reduced, but hardware resource requirements increase due to inefficient bin buffer management
Solution Approach 1:
The patent applies dynamics by making the bin buffer size adaptive rather than fixed. The buffer size is dynamically adjusted based on the actual number of bins generated during entropy encoding, allowing the system to optimize hardware resource usage according to the specific characteristics of the video data being processed, thereby reducing overall hardware requirements while maintaining compression efficiency
Solution Approach 2:
The patent changes the parameter of bin buffer size from a static value to a variable that adapts based on encoding conditions. By modifying this parameter dynamically according to the number of bins generated, the system achieves better compression performance without proportionally increasing hardware resource consumption
2Measurement precision
If video resolution and frame rate are increased, then image quality is improved, but data volume to be encoded increases significantly
Solution Approach 1:
The patent applies segmentation by dividing the video data into blocks and processing each block independently through entropy encoding. This block-based approach allows for more efficient compression by applying context-adaptive binary arithmetic coding (CABAC) to each segment, reducing the overall data volume while maintaining high image quality through precise local compression
Solution Approach 2:
The patent implements local quality by applying different entropy encoding strategies to different blocks based on their specific characteristics. Each block is encoded with optimal compression parameters tailored to its content, allowing high-resolution and high-frame-rate video to be compressed more efficiently without compromising overall image quality
3Productivity
If bin buffer size is increased to handle high-resolution video, then encoding capacity is improved, but hardware resource consumption increases
Solution Approach 1:
The patent resolves this contradiction by making the bin buffer size dynamic rather than statically large. The buffer size adapts to the actual encoding needs of each video sequence, providing sufficient capacity for high-resolution video when required while minimizing hardware resource consumption during normal operation, thus improving encoding capacity without proportionally increasing hardware consumption
Data Source
AI summary
A method is executed to efficiently operate a bin buffer to limit a bin-to-bit ratio in entropy encoding and decoding related to bitstream generation and parsing. In addition, a method of configuring a list includes various entropy encoding/decoding methods and adaptively uses the entropy encoding/decoding methods for each basic unit of entropy encoding/decoding.