Adaptive Entropy Coding for Video Block Compression
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Solution Overview
Problem
Current video encoding and decoding techniques face inefficiencies in compressing video streams due to high data requirements and dependency on fixed scan orders and intra-prediction modes, leading to increased bit rates and computational costs.
Innovation Solution
The implementation of adaptive entropy coding systems that partition video data into groups, using context selection and scan order adaptation based on the distribution of non-zero coefficients within the current block, to improve coding efficiency and reduce dependency on external parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed scan orders and intra-prediction modes are used, then encoding simplicity is maintained, but coding efficiency deteriorates and bit rates increase
Solution Approach 1:
The patent applies dynamics by making the scan order adaptive rather than fixed. The scan order is dynamically selected from multiple candidate scan orders based on the characteristics of the current block (such as the position of non-zero coefficients), allowing the encoding process to adapt to different block patterns and improve coding efficiency while maintaining reasonable complexity.
Solution Approach 2:
The patent changes the parameter of scan order from a fixed value to a selectable set of values. By defining multiple candidate scan orders (e.g., different zig-zag patterns) and selecting the most appropriate one based on block characteristics, the system achieves better compression performance without requiring complex optimization during encoding.
2Productivity
If adaptive entropy coding with context selection is implemented, then coding efficiency improves, but device complexity increases
Solution Approach 1:
The patent segments the block into regions based on the position of non-zero coefficients and applies different scan orders to different segments. This segmentation allows the system to focus computational effort on the most important parts of the block while using simpler methods for other parts, thereby improving overall coding efficiency without proportionally increasing complexity.
Solution Approach 2:
The patent applies local quality by selecting scan orders based on local characteristics of the block (such as where non-zero coefficients are located). Different parts of the block can use different scan orders optimized for their specific patterns, rather than applying a single scan order to the entire block, which improves compression while keeping the decision process relatively simple.
3Device complexity
If fixed encoding methods are used, then computational complexity is reduced, but bit rates increase due to less efficient compression
Solution Approach 1:
The patent introduces dynamic selection of scan orders based on block characteristics, allowing the encoder to adapt to different patterns of non-zero coefficients. This dynamic approach improves compression efficiency by matching the scan order to the actual data distribution, thereby reducing bit rate without requiring complex optimization algorithms.
Solution Approach 2:
The patent performs preliminary analysis of the block (such as identifying the position of non-zero coefficients) before selecting the scan order. This preliminary action allows the system to make informed decisions about which scan order to use, improving compression efficiency without adding complex real-time optimization during the actual encoding process.
Data Source
AI summary
Systems and methods are disclosed for entropy coding of blocks of image data. For example, methods may include partitioning a block of video data into a plurality of groups of elements; decoding, using an entropy decoder, data from an encoded bitstream to obtain, using a first scan order, elements of a first group from the plurality of groups of elements, wherein the first group includes elements forming a triangle in a corner of the block, and wherein the triangle includes elements of a first row and elements of a first column of the block; determining, based on the elements of the first group, a second scan order for a second group from the plurality of groups of elements; and decoding, using the entropy decoder, data from the encoded bitstream to obtain, using the second scan order, elements of the second group from the plurality of groups of elements.