SEI-Based Image Encoding for High-Resolution Bit Reduction
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images leads to a surge in transmission and storage costs due to the increased amount of information, necessitating high-efficiency image compression technology.
Innovation Solution
The implementation of a method that includes acquiring and decoding Supplemental Enhancement Information (SEI) messages for images, and deriving and encoding these images to enhance encoding and decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image resolution and quality are improved, then image quality is improved, but transmission and storage costs increase
Solution Approach 1:
The patent segments image data into multiple picture groups and processes them independently using parallel processing units. Each picture group is divided into smaller processing blocks that can be handled separately, allowing efficient compression of high-resolution images without requiring proportional increases in transmission and storage resources
Solution Approach 2:
The patent employs dynamic adjustment of processing parameters including variable block sizes, adaptive transformation types, and flexible quantization strategies. The system dynamically selects processing depth and complexity based on image characteristics, enabling optimal compression ratios while maintaining high image quality
2Measurement precision
If image resolution and quality are improved, then image quality is improved, but the number of bits to be transmitted increases
Solution Approach 1:
The patent extracts and removes redundant information from high-resolution images through multiple processing stages including prediction, transformation, and quantization. By identifying and eliminating correlated data patterns, the system maintains image quality while significantly reducing the number of bits required for transmission
Solution Approach 2:
The patent transforms image data from spatial domain to frequency domain using various transformation techniques, changing the representation parameters to enable more efficient compression. The system adjusts transformation types, block sizes, and quantization parameters to optimize the balance between image quality preservation and bit reduction
3Productivity
If parallel processing is increased, then processing speed is improved, but device complexity increases
Solution Approach 1:
The patent divides the parallel processing system into multiple independent processing units, each handling specific picture groups or processing blocks. This segmentation allows the system to achieve high processing speeds through parallel execution while keeping individual processing unit complexity manageable and enabling modular system design
Data Source
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AI summary
A method according to embodiments may include acquiring a Supplemental Enhancement Information (SEI) message for one or more pictures, and decoding the one or more pictures. A method according to embodiments may include deriving an SEI message for one or more pictures, and encoding one or more pictures.