Hierarchical Entropy Coding for Run-Level Coefficient Compression
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Solution Overview
Problem
Existing video encoding techniques fail to efficiently exploit correlations between run count and level values in run-level coding, leading to suboptimal entropy coding performance due to large codebook sizes and varying efficiency across different frequency AC coefficients.
Innovation Solution
The proposed solution involves encoding data hierarchically as a set with subsets, using symbol combination codes that indicate set symbols, and adapting codebooks based on data value distributions, allowing for efficient encoding and decoding of quantized transform coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If joint encoding of run count and level value pairs is used to exploit correlation, then coding efficiency is improved, but codebook size and lookup complexity increase significantly
Solution Approach 1:
The patent segments the joint run count/level value encoding into separate encoding processes. Instead of using a single large codebook for joint pairs, it uses multiple smaller codebooks: one for run counts and separate ones for level values. This segmentation reduces the complexity of lookup operations while still exploiting correlations through context-dependent codebook selection.
Solution Approach 2:
The patent implements dynamic codebook selection based on the context of the data being encoded. Different codebooks are selected depending on the run count value or other contextual information, allowing the system to adapt to local correlations without requiring a single exhaustive codebook. This dynamic approach maintains coding efficiency while reducing overall complexity.
2Device complexity
If escape codes are used to control codebook size, then device complexity is reduced, but coding efficiency decreases due to loss of correlation exploitation
Solution Approach 1:
Rather than using escape codes in a single joint encoding scheme, the patent segments encoding into multiple specialized codebooks. This allows each codebook to be optimized for its specific purpose without needing escape mechanisms, reducing the need for complex escape code handling while maintaining correlation exploitation through context-dependent selection.
Solution Approach 2:
The patent changes the parameter of codebook organization from joint pairs to separate distributions with context-dependent selection. This parameter change allows the system to exploit correlations through selective codebook usage rather than through escape codes, maintaining efficiency while reducing complexity.
3Device complexity
If the same code values are used for run-level combinations across different AC coefficient frequencies, then device complexity is simplified, but coding efficiency deteriorates due to inability to adapt to frequency-specific characteristics
Solution Approach 1:
The patent applies local quality by using different codebooks for different contexts, specifically for different AC coefficient frequencies or positions. Each context can have its own optimized codebook that exploits the specific statistical characteristics of that local region, improving coding efficiency without requiring a completely different encoding system for each case.
Solution Approach 2:
The system dynamically selects appropriate codebooks based on the frequency or position of AC coefficients being encoded. This dynamic adaptation allows the encoder to match the statistical properties of different frequency regions with appropriate codebooks, improving efficiency while maintaining a unified overall encoding framework.
Data Source
AI summary
Techniques and tools for encoding and decoding data values that are hierarchically organized are presented. For example, an encoder encodes data as a set that has a hierarchy of subsets with set symbols. In the encoding, the encoder evaluates the data values of the set and selectively encodes a symbol combination code that indicates the set symbols of multiple subsets of the set. Then, for each of the multiple subsets considered as a new set, the encoder selectively repeats the evaluating, selective encoding and selective repetition for the new set. In corresponding decoding, a decoder decodes data encoded as a set that has a hierarchy of subsets with set symbols. In some implementations, the encoding and decoding are adaptive and use a symbol alphabet with nested elements.