Joint Color Channel Entropy Encoding for Display Image Quality
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Solution Overview
Problem
Existing image processing systems face challenges in reducing motion blur and ghosting effects in flat-panel displays due to the inability to keep pace with rapid data changes, leading to undesirable effects like motion blur and ghosting, which current image compensation technologies do not adequately address.
Innovation Solution
A method involving joint color channel entropy encoding with positive reconstruction error, where separate color channel quantization branches generate quantized indices for each pixel, ensuring non-negative reconstruction error, and an entropy encoder maps these indices to entropy encoding indices using techniques like Exponential-Golomb or Golomb-Rice encoding, while exploiting inter-channel correlations to reduce storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate color channel quantization branches are used to guarantee non-negative reconstruction error, then image quality is improved by reducing motion blur and ghosting effects, but device complexity increases due to multiple quantization branches and entropy encoding processes
Solution Approach 1:
The encoding system is divided into separate color channel quantization branches, with each branch independently processing one color channel to guarantee non-negative reconstruction error for that channel. This segmentation allows each branch to be optimized for reliability while maintaining overall system functionality.
Solution Approach 2:
Multiple quantization branches processing different color channels are merged through joint entropy encoding. The entropy encoder combines the quantized indices from all color channels and applies entropy coding to the combined data, reducing overall complexity by sharing the entropy encoding process across channels while maintaining the benefits of separate quantization.
2Quantity of substance
If joint color channel entropy encoding is implemented, then storage requirements are reduced through efficient encoding, but manufacturing precision requirements increase due to the complexity of implementing accurate entropy encoding mappings
Solution Approach 1:
Entropy encoding mappings are pre-computed and stored in lookup tables during an offline training phase. The frequency of occurrence for each n-tuple is calculated in advance, and the corresponding entropy encoding indices are determined beforehand. This preliminary action eliminates the need for complex real-time calculations during encoding, reducing implementation precision requirements while maintaining high compression efficiency.
Solution Approach 2:
The complex entropy encoding process is replaced by copying pre-computed mapping data from lookup tables. During encoding, the system simply retrieves pre-calculated entropy encoding indices based on the quantized n-tuples, avoiding the need to perform complex entropy encoding calculations in real-time and significantly reducing manufacturing precision requirements.
3Productivity
If n-tuples of quantized indices are mapped to entropy encoding indices based on frequency of occurrence, then encoding efficiency is improved, but loss of information increases due to quantization of color channels
Solution Approach 1:
The quantization step size and precision are adjusted based on the specific requirements of each color channel and the desired balance between compression efficiency and information preservation. By optimizing quantization parameters for different channels and using joint entropy encoding to capture correlations, the system achieves high encoding efficiency while minimizing color information loss through intelligent parameter selection rather than uniform quantization.
Data Source
AI summary
A method includes receiving an image frame comprising pixels, wherein each pixel comprises n color channels, generating, by a separate color channel quantization branch for each of the n color channels configured to guarantee a non-negative reconstruction error, a first sample set of quantized indices based on color values of residues for a first particular pixel in the image frame, wherein the first sample set corresponds to a first n-tuple, and generating, by an entropy encoder, an entropy encoding for the first particular pixel based on data that maps indices to n-tuples of quantized indices, wherein the first n-tuple is mapped to a first index.