Unprocessed Image Coding and Decoding for High-Bit Fidelity
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Solution Overview
Problem
Current methods are inadequate for compressing and decompressing high-bit unprocessed image frame data with high fidelity, especially for multiple frames, leading to reduced perceptual quality and memory bandwidth issues.
Innovation Solution
The method involves transforming the image domain, block processing, quantization, and entropy encoding for compression, and inverse operations for decompression, utilizing a hybrid computational system with FPGA, GPU, and CPU to maintain perceptual fidelity and reduce storage size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If 8-bit data compression is used for unprocessed image frames, then compression is achievable, but high-bit unprocessed image frame data cannot be compressed with high fidelity
Solution Approach 1:
The patent changes the data representation parameter from 8-bit to 10-bit color space, enabling high-bit unprocessed image frame data to be compressed while maintaining high fidelity. This parameter change allows the compression algorithm to work effectively with higher bit depths, resolving the contradiction between compression fidelity and bit depth compatibility
2Manufacturing precision
If high-bit unprocessed image frame data is compressed, then compression fidelity can be maintained, but memory bandwidth issues arise
Solution Approach 1:
The patent transforms the data from 10-bit color space to 8-bit grayscale, effectively reducing the data volume while preserving perceptual quality. This parameter transformation allows the system to maintain high perceptual quality during compression while significantly reducing the memory bandwidth requirements for handling multiple frames
3Reliability
If multiple frames are processed, then comprehensive image data is achieved, but processing efficiency decreases
Solution Approach 1:
The patent applies parameter changes by converting multiple frames from 10-bit color space to 8-bit grayscale format, which reduces the data processing complexity and memory bandwidth requirements. This enables more frames to be processed efficiently while maintaining complete image data, thus resolving the contradiction between data completeness and processing speed
Data Source
AI summary
A method of image compression, including, receiving at least one unprocessed image frame, transforming a domain of the at least one unprocessed image frame to output a transformed domain dataset, block processing the transformed domain dataset to yield a blocked dataset, quantizing the blocked dataset to produce a quantized dataset and entropy encoding the quantized dataset to construct at least one compressed image frame.