Pixel Block Compression With Region-Specific Quantization

Solution Overview

Problem

Existing data compression techniques for computing devices face challenges in achieving optimal compression ratios and power efficiency, particularly in lossless and lossy compression methods, which affect performance and circuit area, and there is a need for adaptive techniques that can switch between lossless and lossy compression based on target output size.

Innovation Solution

The implementation of a compression circuitry that dynamically selects between lossless and lossy compression techniques by using multiple predictors to determine the best compression method for a block of pixels, allowing for different delta widths in various regions and incorporating bias and decorrelation to improve compression ratios, and falling back to lossy compression if lossless methods cannot meet a target size.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If lossless compression techniques are used, then data accuracy is maintained, but compression ratio and circuit area increase

Engineering Contradiction:

Improvedata accuracyVSAvoidcircuit area

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The compression circuit dynamically selects between lossless and lossy compression techniques based on target output size requirements. The circuit can switch between different compression modes (lossless, lossy with color quantization, lossy with chroma subsampling) to adapt to different compression needs, optimizing the balance between data accuracy and compression ratio.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes compression parameters dynamically by adjusting the compression technique selected (lossless vs. lossy), the number of bits per pixel (color quantization), and the chroma subsampling level. These parameter changes allow the circuit to achieve different compression ratios while maintaining acceptable image quality.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If lossless compression techniques are used, then data accuracy is maintained, but power consumption increases

Engineering Contradiction:

Improvedata accuracyVSAvoidpower consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The compression circuit dynamically selects between lossless and lossy compression techniques based on target output size requirements. The circuit can switch between different compression modes (lossless, lossy with color quantization, lossy with chroma subsampling) to adapt to different compression needs, optimizing the balance between data accuracy and compression ratio.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes compression parameters dynamically by adjusting the compression technique selected (lossless vs. lossy), the number of bits per pixel (color quantization), and the chroma subsampling level. These parameter changes allow the circuit to achieve different compression ratios while maintaining acceptable image quality.

Inventive Principle:

Principle #35Parameter changes

3Quantity of substance

If higher compression ratios are achieved, then data size is reduced, but compression speed decreases

Engineering Contradiction:

Improvedata sizeVSAvoidcompression speed

Core Design Contradiction:

Quantity of substanceVSProductivity

Solution Approach 1:

The compression circuit processes image data in 4x4 pixel blocks, dividing the overall compression task into smaller, independent segments. This block-based processing allows for efficient parallel operation and reduces the computational complexity per block, improving compression speed while maintaining high compression ratios through techniques like run-length encoding and differential encoding within each block.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The compression circuit dynamically selects between lossless and lossy compression techniques based on target output size requirements. The circuit can switch between different compression modes (lossless, lossy with color quantization, lossy with chroma subsampling) to adapt to different compression needs, optimizing the balance between data accuracy and compression ratio.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS11664816B2Lossy compression techniques

Publication Date: 2023.05.30 APPLE INC

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.