Luminance Chrominance Quality Control Image Compression
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Solution Overview
Problem
Existing digital image compression techniques, such as JPEG2000, suffer from subjective quality control issues and lack intuitive user interfaces for adjusting compression levels, leading to inadequate representation of perceived image degradation and inefficient file size prediction.
Innovation Solution
A system that separates luminance and chrominance channels in digital images, allowing users to independently set quality numbers for each channel, performs rate-distortion optimizations, and generates previews by selecting bitplanes based on stored rate-distortion information, enabling precise control over image quality and file size without repeated entropy coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy compression techniques such as JPEG2000 are used to reduce image size, then file size is reduced, but image quality deteriorates and user control over quality is lost
Solution Approach 1:
The patent segments the image into luminance and chrominance channels, allowing independent quality control for each channel. This segmentation enables users to preserve luminance quality while reducing chrominance quality, achieving file size reduction with maintained perceptual image quality.
Solution Approach 2:
The patent applies different quality levels to different channels (luminance and chrominance) based on human visual system characteristics. By allocating higher quality to luminance channels where the human eye is more sensitive and lower quality to chrominance channels, the system achieves efficient compression while maintaining perceived image quality.
2Manufacturing precision
If PCRD technique with hard-coded weights is used to control compression, then distortion is minimized for target bitrate, but user control and flexibility are reduced
Solution Approach 1:
The patent transforms the static, hard-coded weight system into a dynamic, user-controllable system. Users can adjust quality numbers for luminance and chrominance channels independently, and the system dynamically calculates target bitrates and applies rate-distortion optimization accordingly, providing both precision and flexibility.
Solution Approach 2:
The patent introduces user-adjustable quality numbers as controllable parameters for luminance and chrominance channels. These parameters directly influence the target bitrate calculation and rate-distortion optimization process, enabling users to control compression quality without dealing with complex hard-coded weight configurations.
3Measurement precision
If repeated entropy coding is performed to generate previews for different quality settings, then accurate file size prediction is achieved, but processing time increases significantly
Solution Approach 1:
The patent performs entropy coding once on the maximally compressed bitstream in advance, storing the result for reuse. When generating previews for different quality settings, the system selectively discards bitplanes from this pre-coded bitstream rather than performing repeated entropy coding, dramatically reducing preview generation time while maintaining accurate file size predictions.
Data Source
AI summary
A system that compresses a digital image. During operation, the system obtains a digital image in which the image components are de-correlated into separate channels containing luminance information and chrominance information. Next, the system receives a quality number for the luminance channel and a quality number for the chrominance channels, and uses the quality numbers to determine a target bitrate for the luminance channel and for the chrominance channels, respectively. Given the target bitrate for the luminance channel and the target bit rate for the chrominance channels, the system performs rate distortion optimizations separately on the luminance and chrominance channels to determine which portions of the luminance bitstream and which portions of the chrominance bitstream are to be included in the final bitstream. The system then uses the stored rate-distortion information to generate a preview of the digital image without performing entropy coding each time the quality settings are changed.