Hybrid Image Coding Modes for Screen Region Compression
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Solution Overview
Problem
Current image compression technologies face challenges in efficiently compressing computer screen images due to the large amount of data required for real-time transmission, particularly with the HEVC standard, as they struggle to effectively utilize the diverse characteristics of various image regions, leading to suboptimal compression effects.
Innovation Solution
An image coding method that performs hybrid coding using multiple coding modes, including palette coding, block copy coding, micro-block copy coding, line copy coding, string copy coding, and rectangle copy coding, to fully exploit the characteristics of different image regions, thereby improving compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video coding standards (MPEG-1/2/4, H.264/AVC, HEVC) are used to compress screen images, then some compression is achieved, but the compression efficiency is insufficient for real-time transmission of high-resolution screen images
Solution Approach 1:
The image is divided into coding blocks (CBs) of 8x8 pixels, which are further segmented into prediction units (PUs) and transform units (TUs). This segmentation allows different coding modes to be applied to different regions, improving compression efficiency while maintaining manageable data complexity for transmission
Solution Approach 2:
The patent implements dynamic adaptation of coding modes based on image characteristics. The encoder selects from multiple coding modes (intra prediction, inter prediction, transform coding, palette coding) depending on the content of each coding block, enabling optimal compression efficiency for varying image types while controlling output data rate
2Productivity
If multiple coding modes are applied to different image regions, then compression efficiency is improved, but the complexity of the coding system increases
Solution Approach 1:
The coding system is segmented into distinct functional units: prediction units for generating predictions, transform units for applying transforms, and coding units for final encoding. Each unit handles a specific aspect of compression, making the complex multi-mode system manageable through modular architecture
Solution Approach 2:
The system manages complexity by changing parameters such as coding mode selection, prediction residue generation, and transform type based on image characteristics. Rather than implementing all possible operations simultaneously, the system dynamically adjusts parameters to achieve optimal compression for each region
Data Source
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AI summary
Disclosed is an image coding method, including: determining a coding mode of a coding block; and performing coding on the coding block using multiple coding modes, including performing coding on pixel sample segments in the coding block using one of the multiple coding modes. Further disclosed is an image decoding method, including: parsing bitstream data of a decoding block, and determining decoding modes of pixel sample segments of the decoding block; and performing hybrid decoding on the decoding block using multiple decoding modes, including performing decoding on the pixel sample segments of the decoding block using corresponding decoding modes. The technical scheme described above can fully exploit and utilize the characteristics of each coding image region in the image to improve the image compression performance.