Bicriteria Block Splitting Heuristic for Lossy Image Compression
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Solution Overview
Problem
Existing image compression techniques using lossy compression methods often introduce visual artifacts such as ringing and banding, which degrade image quality, especially at higher compression levels.
Innovation Solution
A method for partitioning an image block to reduce quantization artifacts by estimating the expected entropy of the block, partitioning it into sub-blocks, calculating visual masking amounts, selecting the highest visual masking value, combining it with the block's entropy to obtain a splitting indicator, and determining whether to split the block based on this indicator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If lossy compression techniques are applied to reduce image data size, then compression level is improved, but visual artifacts such as ringing and banding increase
Solution Approach 1:
The image is divided into multiple blocks, and each block is further partitioned into sub-blocks based on visual masking characteristics and entropy calculations. This segmentation allows different compression strategies to be applied to different regions, reducing overall artifacts while maintaining compression efficiency.
Solution Approach 2:
The patent applies different compression levels to different blocks based on their local visual masking characteristics and entropy values. Blocks with high visual masking or low entropy receive higher compression, while blocks with low visual masking or high entropy receive lower compression to minimize artifacts in visually sensitive areas.
2Object-affected harmful factors
If block partitioning is performed to reduce quantization artifacts, then artifact reduction is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary calculations of visual masking characteristics and expected entropy for each block before final partitioning decisions are made. This preliminary analysis allows the system to pre-determine which blocks should be partitioned and at what level, reducing the computational burden during the actual compression process.
Solution Approach 2:
The patent uses visual masking values and entropy measurements as parameters to dynamically adjust block partitioning strategies. By changing these parameters based on local image characteristics, the system optimizes artifact reduction while avoiding unnecessary partitioning in regions where it would not provide benefit, thus controlling computational complexity.
Data Source
AI summary
A method for partitioning a block of an image to reduce quantization artifacts includes estimating an expected entropy of the block; partitioning the block into sub-blocks, where each sub-block having a size of a smallest possible partition size; calculating respective amounts of visual masking for the sub-blocks; selecting, as a visual masking characteristic of the block, a highest visual masking value of the respective amounts of visual masking for the sub-blocks; combining the visual masking characteristic of the block and the expected entropy of the block to obtain a splitting indicator value; and determining whether to split the block based on the splitting indicator.