Pixel Block Compression With Region-Specific Quantization
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Solution Overview
Problem
Existing data compression techniques for computing devices face challenges in achieving optimal compression ratios and power efficiency, particularly in lossy compression methods that may compromise data accuracy and require significant computational resources.
Innovation Solution
The proposed solution involves a hybrid approach that dynamically selects between lossless and lossy compression techniques based on target output size, using multiple predictors to determine the best compression method for pixel data blocks, allowing for variable bit widths and region-specific compression to optimize compression ratios and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lossless compression techniques are used, then data accuracy is maintained, but compression ratio and power efficiency deteriorate
Solution Approach 1:
The system dynamically selects between lossless and lossy compression techniques based on block characteristics and target output size requirements. The compression mode is not fixed but adapts in real-time, allowing the system to use lossless compression only when necessary for blocks requiring high accuracy while using lossy compression for other blocks to reduce power consumption.
Solution Approach 2:
Different compression techniques are applied to different blocks of pixel data based on their specific characteristics. Some blocks may require lossless compression to maintain data accuracy, while other blocks can tolerate lossy compression. This localized approach allows the system to optimize power consumption overall while maintaining accuracy where needed.
2Use of energy by moving object
If lossy compression techniques are used, then compression ratio and power efficiency improve, but data accuracy deteriorates
Solution Approach 1:
The system adjusts compression parameters such as quantization precision and bit depth based on block characteristics and target output size. By dynamically changing these parameters, the system can achieve acceptable data accuracy while maximizing compression ratio and power efficiency for each specific block.
Solution Approach 2:
The compression technique selected is dynamic and adapts to the specific requirements of each block and the overall target output size. The system can switch between lossless and lossy modes, and adjust lossy compression parameters dynamically, rather than using a fixed compression approach throughout.
3Quantity of substance
If multiple predictors are used to determine best compression method, then compression ratio improves, but device complexity increases
Solution Approach 1:
The pixel data is divided into multiple blocks, and different compression techniques can be applied to different blocks. Multiple predictors are evaluated for each block independently, allowing the system to achieve high compression ratios through selective application rather than requiring all predictors to be fully implemented simultaneously throughout the entire data set.
Solution Approach 2:
The system evaluates multiple predictors and compression techniques but does not necessarily implement all of them fully. Instead, it selects the most appropriate technique for each block based on the evaluation, using partial action (evaluating multiple options but implementing only the best one) to achieve high compression ratios without the full complexity of implementing all predictors simultaneously.
Data Source
AI summary
Techniques are disclosed relating to compression of pixel data using different quantization for different regions of a block of pixels being compressed. In some embodiments, compression circuitry is configured to determine, for multiple components included in pixels of the block of pixels being compressed, respective smallest and greatest component values in respective regions of the block of pixels. The compression circuitry may determine, based on the determined smallest and greatest component values, to use a first number of bits to represent delta values relative to a base value for a first component in a first region and a second, different number of bits to represent delta values relative to a base value for a second component in the first region. The compression circuitry may then quantize delta values for the first and second components of pixels in the first region of the block of pixels using the determined first and second numbers of bits. In some embodiments, the compression circuitry determines whether to provide cross-component bit sharing within a region.