Visual Lossless Image Compression via Fixed-Rate Pixel Coding
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Solution Overview
Problem
Current perceptual coding techniques for high-end displays and AR/VR devices face challenges in managing data bandwidth and power consumption while maintaining visual quality, often resulting in significant noise, color tone changes, and banding artifacts due to visible compression distortions.
Innovation Solution
A coding module that employs a fixed-rate compression and decompression process, utilizing raw and differential coding modes to selectively compress pixels based on their activity, ensuring visually lossless image/video compression by encapsulating coding modes within compressed pixel values, thereby reducing memory overflow and maintaining consistent data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If perceptual coding techniques are used to compress image data for high-end displays, then data bandwidth is reduced, but visual quality deteriorates with visible compression artifacts
Solution Approach 1:
The patent applies different coding modes (raw coding vs. differential coding) to different pixels based on their local characteristics. Active pixels with high variation use raw coding to preserve quality, while inactive pixels with low variation use differential coding to reduce bandwidth. This local differentiation resolves the contradiction by optimizing each pixel's compression based on its specific visual importance.
Solution Approach 2:
The patent dynamically changes the compression parameter (coding mode) based on pixel activity level. By monitoring pixel variation and switching between raw and differential coding modes, the system adapts the compression strength to maintain visual quality where needed while reducing bandwidth where possible.
2Quantity of substance
If variable-rate compression is used to reduce data size, then bandwidth consumption is reduced, but memory management becomes problematic with overflow risks
Solution Approach 1:
The patent enforces a fixed bit depth (e.g., 10 bits) for all compressed pixel values, creating homogeneous data structures. This uniformity eliminates variable-rate compression's memory management issues by ensuring consistent memory allocation and preventing overflow, while still achieving compression through the selective application of coding modes.
3Use of energy by moving object
If compression is applied to reduce power consumption during data transmission, then energy usage is reduced, but compression artifacts become visible
Solution Approach 1:
The patent applies compression selectively based on local image characteristics. Inactive regions with uniform pixel values undergo more aggressive differential coding to reduce power consumption, while active regions with edges or details use raw coding to preserve quality. This local differentiation resolves the power-quality tradeoff.
Solution Approach 2:
The patent converts the potential harm of compression artifacts into a benefit by using pixel activity detection. Regions that would normally show artifacts (inactive regions) are identified and compressed more aggressively, while regions sensitive to artifacts (active regions) are protected. The compression process itself becomes beneficial by focusing effort where it matters least.
4Productivity
If high compression ratios are used to manage bandwidth for 4K displays, then data transmission efficiency is improved, but color tone accuracy deteriorates
Solution Approach 1:
The patent preserves color tone accuracy in regions where it matters most (active pixels with detail) by using raw coding, while applying more aggressive compression in uniform regions where color tone is less critical. This selective approach maintains overall color accuracy while achieving high compression ratios for efficient transmission.
Data Source
AI summary
A data compression method is provided for compressing an image. A coding module may select a plurality of pixels with a sequence order from the image, and compress the plurality of pixels to generate a plurality of compressed pixels. For a current pixel p[i] having a previous pixel p[i−1] and a next pixel p[i+1], the coding module generates a coding mode M[i+1] configured for compressing the p[i+1], and generates a fixed-rate compressed value c[i] corresponding to the p[i]. The coding module stores the c[i] in a compressed pixel, and c[i] encapsulates the coding mode M[i+1]. The coding module then stores the plurality of compressed pixels into a compressed image corresponding to the image.