Progressive Color Splitting for Palette Image Compression
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Solution Overview
Problem
Current progressive encoding methods for palette images, such as GIF and PNG, do not achieve optimal compression ratios and do not refine color information progressively to lossless recovery, especially over low-bandwidth communication links.
Innovation Solution
A method involving a tree structure is used to progressively encode digitized color images by initializing a tree structure with starting leaf nodes, determining representative colors, and growing the tree by selecting nodes to split based on achievable cost, which considers distortion and entropy rate changes, to encode index information and pixel data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If interlaced GIF or PNG format is used for progressive transmission, then users can view complete versions of images at low resolution before all data is received, but the color information of each pixel is not refined progressively to lossless recovery
Solution Approach 1:
The color information refinement process is segmented into multiple passes, where each pass refines specific color components (e.g., red, green, blue channels separately) rather than all colors simultaneously. This allows progressive color refinement while maintaining efficient compression and enabling early viewing of complete low-resolution images.
2Measurement precision
If MCQ method is used for progressive color information refinement, then bit-plane by bit-plane refinement allows early recognition of shapes with fine details, but the compression ratio is only comparable to GIF or PNG format
Solution Approach 1:
Different refinement strategies are applied to different color components based on their local importance and characteristics. More significant color components are refined with higher precision while less significant components use coarser refinement, optimizing the balance between color accuracy and compression ratio.
3Quantity of substance
If color splitting algorithm is used to improve compression ratio, then around 30-40% size reduction is achieved compared to GIF or PNG format, but the system complexity increases
Solution Approach 1:
The color splitting algorithm dynamically determines the optimal number of color components to encode based on image characteristics and available bandwidth. The system adapts the refinement depth and color component selection during encoding, allowing flexible trade-offs between compression ratio and computational complexity.
Data Source
AI summary
A method, system and computer program product for progressively encoding a digitized color image is provided. This involves: initializing a tree structure with at least one starting leaf node; determining at least one representative color for each starting leaf node; and growing the tree structure and updating an encoded image by repeatedly (i) selecting a leaf node n to become a non-leaf node n linked to two new leaf nodes based on an associated achievable cost; (ii) creating the two new leaf nodes by re-allocating each color in n; (iii) determining a representative color for each of the two new leaf nodes; and (iv) encoding the index information associated with the selected leaf node n, the representative colors of the two new leaf nodes, and information regarding a plurality of pixels of the digitized color image corresponding to the two representative colors of the two new leaf nodes.