Wrap-Around Wavelet Compression for High-Ratio Image Data
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Solution Overview
Problem
Current data compression techniques, particularly for image data, face challenges in achieving high compression ratios without significant loss of image quality, especially in applications like 8k video displays where lossless compression methods fall short in meeting bandwidth and storage requirements.
Innovation Solution
A lossy data compression method using wrap-around wavelet compression that divides data values into two parts, applying wavelet compression to the most significant bits and selectively appending bits from the least significant bits to achieve a target compression ratio, while maintaining image quality through progressive improvement until lossless results are obtained.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If lossless compression methods are used for image data, then image quality is preserved, but compression ratio is insufficient to meet bandwidth and storage requirements for high-resolution displays like 8k video
Solution Approach 1:
The patent segments the image data into multiple precision levels or layers, where different portions of the image are compressed with different compression ratios. This allows critical regions to maintain higher quality while less critical regions achieve higher compression, thereby achieving both acceptable overall image quality and improved compression ratio for high-resolution displays
Solution Approach 2:
The patent applies different compression quality levels to different local regions of the image based on their importance. Critical regions (such as areas with high visual significance) are compressed with lower ratios to preserve quality, while non-critical regions use higher compression ratios, achieving a balance between overall image quality and compression efficiency
2Loss of information
If higher quality rendering algorithms are used on faster GPUs, then image quality improves, but memory bandwidth consumption increases
Solution Approach 1:
The patent performs compression of image data before it is transferred to or from GPU memory. By compressing the data in advance (preliminary action), the amount of data that needs to be transferred over the memory bandwidth-constrained interface is reduced, allowing higher quality rendering algorithms to run without proportionally increasing memory bandwidth consumption
3Quantity of substance
If data is compressed before storage in memory, then the amount of data transferred to memory is reduced, but compression complexity increases
Solution Approach 1:
The patent divides the compression task into multiple stages or passes, where different compression techniques are applied to different portions of the data. This segmentation allows the system to manage compression complexity by breaking down the overall complex compression into simpler, more manageable sub-tasks that can be executed efficiently
Data Source
AI summary
A lossy method of compressing data, such as image data, which uses wrap-around wavelet compression is described. Each data value is divided into two parts and the first parts, which comprise the most significant bits from the data values, are compressed using wrap-around wavelet compression. Depending upon the target compression ratio and the compression ratio achieved by compressing just the first parts, none, one or more bits from the second parts, or from a data value derived from the second parts, may be appended to the compressed first parts. The method described may be lossy or may be lossless. A corresponding decompression method is also described.