Hybrid Image Codec With Edge-Based Lossy-Lossless Switching
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Solution Overview
Problem
Current image and video compression techniques face challenges in balancing compression ratio and image quality, with lossless compression offering high fidelity but low compression ratio and lossy compression sacrificing detail for efficiency, often requiring a trade-off between the two.
Innovation Solution
Implementing a hybrid codec that selectively uses lossless compression for detailed areas and lossy compression for non-detailed areas, along with a lossy block repair process to improve image quality by re-encoding unchanged blocks using lossless compression, and employing adaptive Golomb encoding to enhance compression ratios with minimal computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If lossless compression is used, then image quality is maintained, but compression ratio is low
Solution Approach 1:
The image is divided into multiple blocks, and each block is independently analyzed to determine whether it contains important features such as edges. This segmentation allows different compression strategies to be applied to different regions, enabling high compression ratios in simple regions while maintaining image quality in complex regions.
Solution Approach 2:
Different compression techniques are applied to different blocks based on their local characteristics. Blocks containing edges or important features are compressed using lossless methods to preserve quality, while blocks without such features use lossy compression to achieve higher compression ratios. This local differentiation resolves the contradiction between overall compression ratio and image quality.
2Productivity
If lossy compression is used, then compression ratio is improved, but image quality deteriorates
Solution Approach 1:
The compression approach is made dynamic and adaptive rather than static. The encoder dynamically selects between lossless and lossy compression for each block based on real-time analysis of edge presence and other features. This dynamic adaptation allows the system to achieve high compression ratios where possible while automatically maintaining quality where needed.
Solution Approach 2:
The compression parameter (lossless vs. lossy) is changed based on the characteristics of each image block. By adjusting this parameter dynamically according to edge detection results and block complexity, the system optimizes the balance between compression ratio and image quality for each region independently.
3Productivity
If hybrid compression is used, then compression ratio increases, but computational complexity increases
Solution Approach 1:
Instead of applying complex analysis and hybrid compression to every block, the system uses a simplified approach: a quick edge detection test to classify blocks as either containing edges or not. This partial application of complex processing only where necessary significantly reduces overall computational complexity while still achieving the benefits of hybrid compression.
Data Source
AI summary
Systems and methods are described herein for implementing a hybrid codec to compress and decompress image data using both lossy and lossless compression. In one example encoding process, it may be determined whether a first block of pixels of a frame of image data contains an edge. A type of compression by which to encode the first block may be selected based on that determination. The first block may be compressed using the selected type of compression. At least one second value associated with the first block of pixels may be set to indicate at least oof the compressed value or the type of compression used to compress the first block.