Adaptive Color Space Transformation for Picture Coding
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Solution Overview
Problem
Current picture coding techniques face challenges in achieving efficient compression of images and video content while maintaining perceptual quality, particularly in high-resolution and bandwidth-limited scenarios, as they often result in decreased picture quality with increased compression rates.
Innovation Solution
The use of a decoder and encoder that transform picture blocks between different color-space representations, such as RGB and YCoCg, to optimize the encoding process, allowing for better quality at a given bit rate by exploiting inherent properties of different color-space representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy compression is applied to reduce file size, then the compressed picture size is decreased, but the picture quality deteriorates
Solution Approach 1:
The patent transforms picture blocks from RGB color space to YCoCg color space before compression. This parameter change in color representation allows for more efficient quantization because the YCoCg transformation separates luminance (Y) and chrominance (Co, Cg) components in a way that aligns better with human perceptual sensitivity, enabling higher compression rates with less quality loss
Solution Approach 2:
The patent applies different quantization strategies to different components and regions. Specifically, it uses asymmetric quantization where the luminance component is preserved with higher precision while chrominance components are quantized more coarsely, matching the local perceptual importance of different picture components
2Quantity of substance
If quantization is applied to reduce storage space, then the amount of storage space is decreased, but the picture quality decreases
Solution Approach 1:
By changing the color space parameters from RGB to YCoCg before quantization, the patent creates a representation where the separation between luminance and chrominance allows for more efficient storage. The transformed parameters enable coarser quantization with less perceptual impact
Solution Approach 2:
The patent strategically discards less important chrominance information through coarser quantization of Co and Cg components while preserving the essential luminance information. The reconstruction process recovers the picture with acceptable quality by leveraging human visual system characteristics
3Productivity
If compression rate is increased to achieve higher file size reduction, then the file size is decreased, but the perceptual quality deteriorates
Solution Approach 1:
The YCoCg color space transformation changes the parameter representation to align with perceptual importance, allowing the compression algorithm to achieve higher rates while maintaining quality by efficiently allocating bits to luminance versus chrominance components
Solution Approach 2:
The patent employs adaptive quantization where the compression algorithm dynamically adjusts quantization step sizes based on local picture characteristics, activity levels, and perceptual importance, enabling optimized compression rates while preserving perceptual quality in critical regions
Data Source
AI summary
The present invention is based on the finding that pictures or a picture stream can be encoded highly efficient when a representation of pictures is chosen that is having different picture blocks, wherein each picture block is carrying picture information for picture areas smaller than the full area of the picture and when the different picture blocks are carrying the picture information either in a first color-space representation or in a second color-space-representation. Since different color-space-representations have individual inherent properties with respect to their describing parameters, choosing an appropriate color-space-representation individually for the picture blocks results in an encoded representation of pictures that is having a better quality at a given size or bit rate.