Frequency-Dependent Transform Coefficient Coding for Low-Latency Compression
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Solution Overview
Problem
Conventional image compression techniques face challenges in achieving high compression with low hardware complexity and latency while maintaining visually lossless results, particularly in encoding and decoding transform coefficients for high-definition images and videos.
Innovation Solution
The embedded codec (EBC) circuitry employs frequency-dependent coding of transform coefficients, grouping them based on their frequency distribution and applying adaptive entropy coding schemes like Progressive Golomb Coding (PGC) to optimize bit allocation and reduce compression artifacts, thereby improving the compression factor and peak signal-to-noise ratio (PSNR).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional entropy coding schemes (e.g., exponential-Golomb coding) are used to encode transform coefficients, then the encoding process is simple and hardware complexity is low, but compression efficiency is insufficient and compression artifacts are prominent
Solution Approach 1:
The transform coefficient block is divided into multiple frequency bands (e.g., low-frequency, mid-frequency, high-frequency bands). Each band is independently entropy-coded using appropriately tuned coding parameters, allowing differential treatment of coefficients with different statistical characteristics. This segmentation enables better compression efficiency while maintaining manageable hardware complexity through modular processing.
Solution Approach 2:
Different entropy coding parameters (such as different Golomb-Rice parameters k values) are applied to different frequency bands based on their local statistical properties. Low-frequency coefficients use one set of parameters while high-frequency coefficients use another, optimizing compression for each region's specific characteristics rather than using a uniform approach across all coefficients.
2Productivity
If advanced compression techniques are applied to achieve high compression ratios, then compression efficiency improves, but hardware complexity and processing latency increase
Solution Approach 1:
The coefficient block is segmented into frequency bands that can be processed in parallel. Each band undergoes independent entropy coding with its own parameter set, allowing the system to achieve advanced compression efficiency through specialized processing while keeping hardware complexity manageable by avoiding a single monolithic complex processor.
Solution Approach 2:
The system dynamically selects and applies different entropy coding parameters (such as varying the Golomb-Rice parameter k) based on the frequency band and coefficient statistics. This parameter adaptation enables high compression efficiency by matching the coding scheme to the data characteristics, while the underlying hardware structure remains relatively simple and reusable across different parameter configurations.
3Productivity
If uniform entropy coding parameters are applied to all transform coefficients, then the encoding process is simple and fast, but compression artifacts increase and visual quality decreases
Solution Approach 1:
The coefficient block is divided into frequency bands (low, mid, high) that are processed separately with band-specific entropy coding parameters. This segmentation allows the system to maintain fast encoding through efficient parallel processing while significantly improving visual quality by applying optimized parameters to each band's specific statistical characteristics, reducing compression artifacts in visually important regions.
Solution Approach 2:
Different entropy coding parameters are applied locally to different frequency bands based on their statistical properties. Low-frequency bands (which contribute more to visual quality) receive parameter optimization that minimizes artifacts, while high-frequency bands use parameters suited for their sparser characteristics. This local adaptation maintains encoding speed through systematic processing while improving overall visual quality.
Data Source
AI summary
Embedded codec (EBC) circuitry for frequency-dependent coding of transform coefficients, groups a plurality of transform coefficients for an input image block into a plurality of groups of transform coefficients. The plurality of transform coefficients are grouped based on a frequency distribution of the plurality of transform coefficients for the input image block. The EBC circuitry selects a different entropy coding parameter from a set of entropy coding parameters for each group of the plurality of groups, based on the frequency distribution. Thereafter, the EBC circuitry applies an entropy coding scheme from a set of entropy coding schemes to each group of transform coefficients, in accordance with the selected entropy coding parameter.