Entropy Coding Context Selection Using Ordered Scan Paths
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Solution Overview
Problem
Existing lossless entropy coding techniques struggle to provide optimal context selection for encoding symbols in image data, often relying on neighboring blocks that are too correlated, leading to inefficiencies in reducing redundancy and increasing complexity.
Innovation Solution
The use of an ordered scan path to select contexts for entropy coding, where current symbols are encoded based on prior encoded symbols from multiple locations within the image, allowing for flexible and more predictive context selection, including non-adjacent blocks, and combining prior symbols into single context values to reduce complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neighboring blocks are used for context selection in entropy coding, then the context is highly correlated with current block, but this leads to increased complexity and reduced efficiency in reducing redundancy
Solution Approach 1:
The patent segments the context selection process into multiple independent stages: (1) generating a set of candidate context locations based on scan path, (2) filtering candidates based on qualification criteria, (3) selecting from qualified candidates. This segmentation allows the system to achieve high context correlation while managing complexity through modular processing steps.
Solution Approach 2:
The patent performs preliminary actions by pre-defining scan paths and qualification criteria before actual entropy coding. The ordered scan path enumerating potential context locations is established in advance, and candidates are pre-filtered based on qualification criteria, reducing the computational burden during the actual coding process.
2Device complexity
If multiple prior symbols are combined into single context value, then complexity is reduced, but the precision of context prediction may be affected
Solution Approach 1:
The patent merges multiple prior symbols from different context locations into a single context value through a systematic combination process. This merging reduces the number of context values that need to be processed while preserving the predictive information from multiple sources, thus reducing complexity without significantly compromising precision.
Solution Approach 2:
The patent changes the parameter representation by transforming multiple individual symbol values into a consolidated context value. This parameter transformation allows the system to work with fewer, more manageable parameters while retaining the essential predictive information needed for accurate entropy coding.
Data Source
AI summary
Improved lossless entropy coding techniques for coding of image data include selecting a context for entropy coding based on an ordered scan path of possible context locations. A symbol for a current location within a source image may be entropy coded based on a context of prior encoded symbols of other locations within source images, where the context is selected based on an ordered scan path enumerating a series of potential context locations within one or more source images. To select a context, a predetermined number of prior symbols may be selected by qualifying or disqualifying locations in the scan path, and then the current symbol may be encoded with a context based on prior symbols corresponding to the first qualifying context locations in the order of the scan path.