Embedded Graphics Coding for Sparse Histogram Image Compression
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Solution Overview
Problem
Conventional image compression schemes are ineffective for "unnatural images" like graphics or text, which have weaker inter-pixel correlation and sparse histograms, leading to poor coding performance and lack of scalability.
Innovation Solution
Embedded Graphics Coding (EGC) method that divides images into blocks, converts pixels to binary representations, and applies context-adaptive prediction and binary run-length coding, allowing for dynamic bit budgeting and partial reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image compression schemes are used for unnatural images, then the coding performance is poor, but the processing complexity remains low
Solution Approach 1:
The image is divided into multiple blocks, and each block is processed independently through sorting and histogram analysis. This segmentation allows the algorithm to handle unnatural images with sparse histograms more effectively by focusing on local pixel distributions rather than global correlations.
Solution Approach 2:
The patent applies preliminary sorting of pixel values within each block before compression. This preliminary action reorganizes the data to create a more favorable distribution for subsequent entropy coding, improving coding performance without adding significant computational complexity.
2Reliability
If histogram packing is used to compress unnatural images, then the compression is achieved, but the bitstream is not scalable and memory cost increases
Solution Approach 1:
The image is divided into multiple blocks that can be independently encoded and decoded. This segmentation enables scalable bitstream processing where partial reconstruction is possible without requiring the entire bitstream, directly addressing the scalability limitation of conventional histogram packing.
Solution Approach 2:
The patent transforms the pixel data representation by sorting pixels within blocks and analyzing local histograms rather than using global histogram packing. This parameter change in the encoding approach reduces memory requirements while maintaining compression effectiveness for unnatural images.
3Reliability
If global histogram packing is applied, then compression is achieved, but partial reconstruction without re-encoding is not possible
Solution Approach 1:
By dividing the image into independent blocks with local histogram analysis, the patent enables progressive decoding where individual blocks can be reconstructed without processing the entire image. This provides scalability and adaptability while maintaining compression effectiveness.
Solution Approach 2:
The patent allows partial reconstruction of the image by decoding only the necessary blocks rather than requiring the complete bitstream. This partial action capability enables scalable delivery and adaptive rendering at different quality levels without full re-encoding.
Data Source
AI summary
Embedded Graphics Coding (EGC) is used to encode images with sparse histograms. In EGC, an image is divided into blocks of pixels. For each block, the pixels are converted into binary representations. For each block, the pixels are scanned and encoded bit-plane by bit-plane from the most significant bit-plane (MSB) to the least significant bit-plane (LSB). The pixels in the block are partitioned into groups. Each group contains pixels with the same value. From the MSB to the LSB, the groups in the current bit plane are processed. During the processing, a group is split into two, if pixels in the group have different bit values in the bit-plane being encoded. Then, the encoder sends the refinement bit for each pixel in the group and the encoder splits the original group into two. A method is described herein to compress the refinement bits which employs context-adaptive prediction and binary run-length coding.