Adaptive Block Pattern Coding for Digital Media Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Block transform-based coding techniques face inefficiencies in encoding block patterns due to varying probability distributions, especially in scenarios with few or many skipped blocks, leading to suboptimal compression results.
Innovation Solution
The proposed digital media codec adapts by modifying block patterns using backward adaptation models and spatial prediction to skew the distribution of zero and non-zero coefficients, enabling effective entropy coding through joint symbol encoding and meta block pattern encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional block transform-based coding is used without adaptation, then the encoding process is simple, but compression efficiency deteriorates due to varying probability distributions of block patterns
Solution Approach 1:
The encoder dynamically adapts to the probability distribution of block patterns by estimating parameters and selecting appropriate entropy coding models based on the observed data characteristics. This allows the system to optimize compression efficiency for different scenarios (e.g., video vs. still images, different bit rates) rather than using a fixed encoding approach.
Solution Approach 2:
The system changes the parameters of the entropy coding model based on the estimated probability distribution of block patterns. By adjusting the coding parameters to match the actual data distribution, the system achieves better compression efficiency while maintaining reasonable encoding complexity.
2Productivity
If entropy coding is applied without adapting to block pattern distribution, then the encoding process is fast, but compression efficiency deteriorates due to suboptimal coding for varying distributions
Solution Approach 1:
The system performs partial adaptation by estimating only the necessary parameters of the block pattern distribution and applying entropy coding adjustments only when beneficial. This selective approach improves compression efficiency without requiring full-blown adaptive modeling that would significantly increase encoding time.
Solution Approach 2:
The encoder uses feedback from the observed block pattern distribution to adjust the entropy coding parameters. By monitoring the actual data characteristics and adapting the coding strategy accordingly, the system achieves better compression ratios while keeping encoding time acceptable through efficient feedback mechanisms.
3Productivity
If block patterns are encoded without joint symbol encoding, then the encoding process is straightforward, but compression efficiency deteriorates due to failure to exploit spatial correlations
Solution Approach 1:
The system merges adjacent block patterns into joint symbols for entropy coding. By combining multiple block patterns into single coding units, the system exploits spatial correlations between neighboring blocks, achieving better compression efficiency while managing coding complexity through the use of adaptive probability models.
4Quantity of substance
If quantization is applied to reduce coefficient precision, then data size is reduced, but signal quality deteriorates due to loss of perceptible frequency components
Solution Approach 1:
The system changes the quantization parameters adaptively based on the estimated probability distribution of block patterns and the observed signal characteristics. By adjusting quantization strength according to the actual data, the system achieves better data compression while minimizing quality loss in perceptible frequency components.
Data Source
AI summary
A block transform-based digital media codec efficiently compresses digital media data using block patterns representing whether a block's coefficients are zero-valued, such that their explicit encoding is skipped. Because the block patterns can have widely varying probability distributions, the codec adaptively chooses a prediction mode for modifying the block patterns (e.g., based on spatial prediction, or inverting) to enhance their compression using entropy coding techniques. Further, with high spatial correlation of block patterns, the codec encodes a meta block pattern for a region indicating whether all block patterns of the region represent zero-valued coefficient blocks. In such cases, the codec can then also omit explicitly encoding the block patterns in those regions.