Digital Data Compression Using GCLI Residual Coding
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Solution Overview
Problem
Existing data compression methods face challenges in reducing the data budget for meta-data and addressing quantization needs, especially when the compressed data set size exceeds a data budget, requiring a method that is simple to implement and efficient in coding meta-data while handling quantization effectively.
Innovation Solution
A method for compressing and decompressing data sets by grouping coefficients, determining the Greatest Coded Line Index (GCLI), performing quantization, and using entropy encoding to reduce meta-data requirements, which includes steps like grouping coefficients, determining GCLI, quantizing, computing residues, and entropy encoding to minimize meta-data size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If GCLI values are coded using binary coding, then the coding is simple, but the meta-data volume is significant
Solution Approach 1:
The patent merges the coding of GCLI values with prediction residuals. Instead of separately coding GCLI values, the method predicts GCLI from previous values and codes only the residuals (differences), combining what would be separate coding operations into a unified residual-based approach that reduces overall meta-data volume.
Solution Approach 2:
The patent changes the parameter being coded from absolute GCLI values to residual values (differences from predicted GCLI). This parameter transformation reduces the magnitude and variability of the data to be coded, enabling more efficient compression and reducing meta-data volume while maintaining the ability to reconstruct original values at the decoder.
2Quantity of substance
If quantization is applied to reduce compressed data set size, then the data budget is satisfied, but the quality is compromised
Solution Approach 1:
The patent applies quantization selectively and partially - only to specific components of the data (such as less significant coefficients or specific bit planes) rather than uniformly to all data. This partial application of quantization achieves size reduction while preserving more quality than full quantization would sacrifice.
3Productivity
If sorting of differences is performed to map parameter values, then the compression efficiency is improved, but additional buffering and processing is required
Solution Approach 1:
The patent performs preliminary organization of data into groups and pre-computation of prediction values before the actual coding stage. By preparing prediction residuals in advance and organizing data into suitable groups, the method eliminates the need for complex sorting operations during compression, reducing device complexity while maintaining efficiency.
Data Source
AI summary
The invention relates to a method for compressing an input data set, wherein the coefficients in the input data set are grouped in groups of coefficients, a number of bit planes, GCLI, needed for representing each group is determined, a quantization is applied, keeping a limited number of bit planes, a prediction mechanism is applied to the GCLIs for obtaining residues, and an entropy encoding of the residues is performed. The entropy-encoded residues, and the bit planes kept allow the decoder to reconstruct the quantized data, at a minimal cost in meta-data.