Progressive Image Compression for Low-Latency Collaborative Sync
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Solution Overview
Problem
Collaborative image editing faces challenges with high latency and pixelation due to frequent data transmission and lossy compression methods, especially when multiple users are editing the same image, leading to increased data synchronization and potential errors.
Innovation Solution
Implementing progressive image compression and syncing using entropy-based pixel prioritization, where visually important regions are prioritized for transmission based on entropy values, and adaptive binary arithmetic coding with context modeling to reduce latency and errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If lossy compression methods are used for frequent data transmission, then bandwidth efficiency is improved, but image reconstruction quality deteriorates
Solution Approach 1:
The patent applies local quality by differentiating compression strategies for different image regions. High-entropy regions (visually important areas with high information content) are prioritized for transmission with higher quality, while low-entropy regions are compressed more aggressively. This is achieved through entropy-based pixel prioritization where the compression algorithm identifies and preserves critical visual information while discarding redundant data, resolving the contradiction between bandwidth efficiency and image quality.
2Productivity
If frequent data transmission is implemented for collaborative editing, then real-time collaboration is improved, but latency increases
Solution Approach 1:
The patent extracts and transmits only the essential visual information from image changes rather than transmitting complete image data. By using entropy-based prioritization, the system identifies and transmits only the high-entropy regions that contain critical visual changes, eliminating redundant low-entropy data. This extraction approach reduces transmission volume and latency while maintaining real-time collaboration capability.
3Manufacturing precision
If complete image data is transmitted for each edit, then image quality is maintained, but bandwidth consumption increases
Solution Approach 1:
The patent changes the parameter of image data representation by using entropy values to characterize different regions of the image. Instead of transmitting complete image data at uniform quality, the system calculates entropy for different pixel regions and uses these entropy values to dynamically adjust compression parameters. High-entropy regions receive higher preservation priority while low-entropy regions are heavily compressed, optimizing bandwidth consumption while maintaining perceptual image quality.
Data Source
AI summary
One or more aspects of the method, apparatus, and non-transitory computer readable medium include obtaining image data and computing a prediction residue value for a pixel of the image data using a prediction function. An entropy value for the pixel can then be determined based on the prediction residue value using context modeling, and progressive compressed image data for the image data can be generated based on the entropy value. The compressed image data can be used to enable collaborative image editing and other image processing tasks.