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 to be predictive, leading to inefficiencies in reducing redundancy.
Innovation Solution
A method for selecting entropy coding contexts based on an ordered scan path of potential locations within a source image, using a predetermined number of prior symbols and combining them into a single context value to enhance predictive encoding, while allowing non-adjacent blocks as context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If neighboring blocks are used as context for entropy coding, then encoding simplicity is maintained, but predictive efficiency deteriorates because neighboring blocks are too correlated to be predictive
Solution Approach 1:
The patent transitions from spatial adjacency (neighboring blocks) to temporal/scan-order sequence (prior blocks in scan path) as the dimension for context selection. This allows using blocks that are not spatially adjacent but appear earlier in the scan order, providing better predictive value while maintaining implementation simplicity through ordered processing.
Solution Approach 2:
The patent introduces an intermediary mechanism (context memory and scan path ordering) between the current block and potential context blocks. This intermediary structure allows selective access to prior blocks based on scan order, filtering out too-correlated neighboring blocks while maintaining encoding simplicity through systematic selection.
2Measurement precision
If more context locations are considered for entropy coding, then predictive efficiency improves, but encoding complexity increases
Solution Approach 1:
The patent segments the image into an ordered scan path, dividing potential context locations into a systematic sequence. This segmentation allows the encoder to process context locations in a predetermined order, selecting a manageable number of prior blocks without overwhelming complexity, while still considering multiple locations for improved prediction.
Solution Approach 2:
The patent applies partial action by selecting a limited number of prior blocks from the scan path rather than using all possible prior blocks as context. This partial selection achieves sufficient predictive efficiency while avoiding the excessive complexity that would result from considering all potential context locations.
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.