Hybrid Entropy Coding for Adjustable Decoder Throughput
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Solution Overview
Problem
Existing entropy coding methods for image content do not allow for adjustable throughput to meet varying design constraints such as available bandwidth, image resolution, and decoder clock speed.
Innovation Solution
The implementation of a hybrid entropy coding scheme that uses symbol variable length codes (SVLC) and common prefix entropy code (CPEC) to encode image content, allowing for adjustable throughput by controlling the number of variable length codes used.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single entropy coding method is used for all samples, then the coding structure is simple, but the throughput cannot be adjusted to meet varying design constraints
Solution Approach 1:
The patent divides the block of samples into two distinct segments: N single samples decoded using SVLC and M sample groups decoded using CPEC. This segmentation allows the system to use different coding methods for different portions of the data, enabling throughput adjustment while maintaining a structured approach to complexity management.
Solution Approach 2:
The patent introduces dynamic parameters N and M that can be adjusted based on target decoder throughput requirements. The values of N and M are calculated dynamically using formulas that consider the total number of samples and the desired throughput, allowing the system to adapt to varying design constraints without requiring a complete redesign of the coding structure.
2Loss of information
If more variable length codes are used, then compression efficiency improves, but decoder throughput decreases
Solution Approach 1:
The patent changes the parameters of the entropy coding system by introducing a hybrid approach that combines SVLC and CPEC with adjustable weights (N and M). By modifying the proportion of samples decoded with each method, the system can optimize the balance between compression efficiency and decoder throughput based on specific application requirements.
Solution Approach 2:
The patent applies different coding qualities to different portions of the data: SVLC provides higher compression efficiency for N single samples, while CPEC provides faster decoding for M sample groups. This local differentiation allows the system to optimize overall performance by placing the most efficient coding method where it is most beneficial.
3Productivity
If N and M are fixed, then the decoding process is simpler, but the system cannot optimize for specific application requirements
Solution Approach 1:
The patent incorporates a feedback mechanism where the system calculates the optimal values of N and M based on the target decoder throughput and total sample count. The calculation formulas provide automatic feedback on how to configure parameters for different throughput requirements, eliminating the need for manual trial-and-error configuration while maintaining optimization capability.
Data Source
AI summary
A method for decoding image content from an encoded bitstream including a plurality of blocks includes: dividing a block including one or more components of the image content into N single samples and M sample groups corresponding to one of the components, where N and M are greater than or equal to one; decoding each of the N single samples using a symbol variable length code to generate one or more decoded single samples; decoding each of the M sample groups using a common prefix entropy code to generate one or more decoded sample groups, each of the M sample groups including a variable length prefix and one or more fixed length suffixes representing a plurality of samples; concatenating the decoded single samples and the decoded sample groups into a block of residuals; and reconstructing image content based on previously reconstructed neighboring blocks and the block of residuals.