Multi-level Significance Map Scanning for Video Encoding
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Solution Overview
Problem
Current video encoding standards, such as H.264/AVC and the developing HEVC, face inefficiencies in encoding and decoding significance maps due to high computational complexity and memory access costs, particularly with larger transform units like 16×16 and 32×32, which result in substantial costs for encoding and transmitting large maps with few coefficient values.
Innovation Solution
The implementation of multi-level significance maps with context-adaptive encoding and decoding methods, where significant-coefficient flags are grouped into blocks and encoded using a multi-level scan order, reducing the need to encode zero flags and optimizing the bitstream by only transmitting non-zero coefficient information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional significance map encoding is used for large transform units (16×16, 32×32), then complete coefficient information is encoded, but computational complexity and memory access costs increase substantially
Solution Approach 1:
The patent extracts and removes zero coefficients from the significance map encoding process. By identifying and excluding zero coefficients that do not contribute to reconstruction quality, the method reduces the number of coefficients that need to be processed, stored, and transmitted, thereby reducing computational complexity and memory access costs while maintaining complete information about non-zero coefficients.
Solution Approach 2:
The patent segments the transform unit into multiple blocks and processes each block separately with its own context model. This segmentation allows the encoder to handle smaller portions of data independently, reducing the overall computational burden and memory requirements compared to processing the entire large transform unit as a single significance map.
2Loss of information
If traditional significance map encoding is used for large transform units, then all coefficient positions are encoded, but bitstream size increases due to transmission of zero coefficient flags
Solution Approach 1:
The patent extracts and removes zero coefficient flags from the bitstream. By identifying positions that contain zero coefficients and excluding them from encoding, the method significantly reduces the number of bits required to represent the significance map, while still preserving complete information about the positions and values of non-zero coefficients through the last significant coefficient position indicator.
3Measurement precision
If separate context models are used for each coefficient position in large transform units, then encoding precision is maintained, but memory access costs and processing time increase
Solution Approach 1:
The patent segments the large transform unit into multiple smaller blocks, each with its own context model. This segmentation reduces the number of context models that need to be maintained and accessed simultaneously, thereby reducing memory access costs and processing time while preserving encoding precision within each block through context-adaptive binary arithmetic coding.
Data Source
AI summary
Methods of encoding and decoding for video data are described in which multi-level significance maps are used in the encoding and decoding processes. The significant-coefficient flags that form the significance map are grouped into contiguous groups, and a significant-coefficient-group flag signifies for each group whether that group contains no non-zero significant-coefficient flags. A multi-level scan order may be used in which significant-coefficient flags are scanned group-by-group. The group scan order specifies the order in which the groups are processed, and the scan order specifies the order in which individual significant-coefficient flags within the group are processed. The bitstream may interleave the significant-coefficient-group flags and their corresponding significant-coefficient flags, if any.