Flexible Coefficient Coding for Video Compression Efficiency
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Solution Overview
Problem
Conventional video codecs face inefficiencies in coefficient coding, particularly at low bitrates, leading to visible distortion and loss of information due to fixed quantization and entropy coding methods that do not adapt well to varying coefficient significance across different regions of a transform unit or prediction unit.
Innovation Solution
The introduction of flexible coefficient coding (FCC) allows for dynamic definition of spatial sub-regions within a transform or prediction unit, enabling variable coefficient groups (VCGs) with different entropy coding approaches, symbol allocations, and context derivation rules, which adapt to the relative distance and location of coefficient samples, improving arithmetic coding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed quantization and entropy coding methods are used, then the coding process is simple and fast, but coding efficiency deteriorates at low bitrates leading to visible distortion and loss of information
Solution Approach 1:
The transform unit is divided into multiple variable coefficient groups (VCGs) based on the significance and location of non-zero coefficients. Each VCG can be encoded with different entropy coding methods and symbol allocations, allowing the system to adapt to varying coefficient distributions within the same block, thereby improving coding efficiency without requiring complete redesign of the entire coding process
Solution Approach 2:
The patent introduces dynamic definition of spatial sub-regions within a transform unit, enabling the coding system to adaptively adjust coefficient group boundaries and encoding parameters based on the actual coefficient significance pattern. This dynamic approach allows the system to optimize coding efficiency for each specific coefficient distribution while maintaining a unified processing framework
2Adaptability or versatility
If fixed entropy coding is used for all coefficients, then the implementation is simple, but it cannot adapt to varying coefficient significance across different regions
Solution Approach 1:
Different entropy coding methods and symbol allocations are applied to different variable coefficient groups based on their local characteristics. Coefficient groups with higher significance or different spatial distributions receive tailored coding parameters, allowing the system to adapt to varying coefficient significance across regions while maintaining a manageable coding framework through localized optimization
3Quantity of substance
If more aggressive quantization is applied to reduce bitrate, then the bitrate is reduced, but visible distortion and loss of information increase
Solution Approach 1:
The patent employs different quantization parameters and entropy coding parameters for different variable coefficient groups. By adjusting these parameters dynamically based on the significance and location of coefficients, the system can optimize the balance between bitrate and information preservation, allowing aggressive quantization in less significant regions while maintaining quality in important regions
Data Source
AI summary
A flexible coefficient coding (FCC) approach is presented. In the first aspect, spatial sub-regions are defined over a transform unit (TU) or a prediction unit (PU). These sub-regions organize the coefficient samples residing inside a TU or a PU into variable coefficient groups (VCGs). Each VCG corresponds to a sub-region inside a larger TU or PU. The shape of VCGs or the boundaries between different VCGs may be irregular, determined based on the relative distance of coefficient samples with respect to each other. Alternatively, the VCG regions may be defined according to scan ordering within a TU. Each VCG can encode a 1) different number of symbols for a given syntax element, or a 2) different number of syntax elements within the same TU or PU. Whether to code more symbols or more syntax elements may depend on the type of arithmetic coding engine used in a particular coding specification. For multi-symbol arithmetic coding (MS-AC), a VCG may encode a different number of symbols for a syntax element. For example, to encode absolute coefficient values inside a TU after performing a transform such as the discrete cosine transform (DCT), a VCG region may be defined around lower-frequency transform coefficients and for that VCG M-symbols can be encoded the absolute coefficient values. Another VCG region can be defined around the higher-frequency transform coefficients to encode K-symbols, where K may be different than M. For binary arithmetic coders (BACs), FCC allows for coding a variable number of syntax elements in different VCGs. In this case, one VCG in a TU may code M-syntax elements associated with signaling the absolute coefficient value, where each one of the M-syntax elements may have 2-symbols. Probability models and context derivation rules may be tailored for each VCG in a given TU or PU. Since each VCG may code a different number of symbols or syntax elements in different spatial locations of a TU or PU, different context models may be used for each VCG to provide better granularity for entropy modeling for arithmetic coding. Furthermore, different VCGs may also use different entropy coders including combinations of arithmetic coding, Golomb-Rice coding, Huffman coding.