Last Significant Coefficient Coding With Coordinate Context Modeling
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Solution Overview
Problem
Current video encoding standards, such as H.264, face challenges in efficiently encoding and decoding the last significant coefficient position due to serial nature of entropy coding methods, which can be computationally demanding and limit processing power, especially in high-quality video decoding applications.
Innovation Solution
The proposed solution involves encoding and decoding the two-dimensional coordinates of the last significant coefficient, where the context of one axis is dependent on the other, using binarization and context modeling to improve efficiency, and modifying the syntax to signal these coordinates efficiently, potentially using fixed-length codes or alternative representations like anti-diagonal lines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If CABAC is used for entropy coding, then compression efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent segments the encoding process by separating the last significant coefficient position encoding from other coefficient data. It uses dedicated context models (ctxIdxLastPosX, ctxIdxLastPosY) specifically for position encoding, while other coefficients use standard CABAC contexts. This segmentation allows optimized processing of the most significant position information without increasing overall complexity.
Solution Approach 2:
The patent performs preliminary binarization of the last significant coefficient position into two separate values (lastPosX and lastPosY) before entropy coding. It pre-determines context models based on transform size and position relationships, preparing the data structure in advance to facilitate more efficient encoding during the actual compression process.
2Device complexity
If serial entropy coding is used, then implementation is simplified, but processing speed decreases
Solution Approach 1:
The patent divides the last significant coefficient position into two independent coordinate values (x and y positions). These can be encoded separately with dedicated context models, allowing parallel or pipelined processing. The segmentation enables the decoder to process position information more quickly without requiring complex sequential dependency resolution.
Solution Approach 2:
The patent transforms the single-dimensional last significant coefficient indicator into a two-dimensional coordinate system (lastPosX, lastPosY). This dimensional change allows for more efficient context modeling by exploiting the spatial relationship between coordinates and enables independent processing of each dimension, improving overall processing throughput.
3Measurement precision
If context modeling is applied to each bin, then encoding precision is improved, but computational demand increases
Solution Approach 1:
The patent applies context modeling selectively rather than uniformly. It uses specific context models (ctxIdxLastPosX, ctxIdxLastPosY) only for the last significant coefficient position bins, while other coefficient data uses standard contexts. This localized application of context modeling provides precision where most needed (position information) without the computational overhead of applying it to all data.
Solution Approach 2:
The patent changes the parameter representation by transforming the last significant coefficient position into two separate binarized values (lastPosX, lastPosY) with different context model indices. This parameter transformation enables more precise context matching for position information while managing computational demand through structured context selection based on transform size and position relationships.
Data Source
AI summary
Methods and devices are described for entropy coding data using an entropy coder to encode quantized transform domain coefficient data. Last significant coefficient information is signaled in the bitstream using two-dimensional coordinates for the last significant coefficient. The context for bins of one of the coordinates is based, in part, upon the value of the other of the coordinates. In one case, instead of signaling last significant coefficient information, the number of non-zero coefficients is binarized and entropy encoded.