Image Signal Compression Using Polar Coding to Limit Error Spreading
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Solution Overview
Problem
Conventional JPEG image compression methods, such as Huffman coding, face challenges in controlling error spreading during decoding, which affects image compression performance.
Innovation Solution
The method employs polar coding to compress image signals, using a polarization operation to separate bits into reserved and discarded sets, and performs channel coding on the reserved set, allowing for fixed-length entropy coding and effective error control during decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If Huffman coding is used for image compression, then compression rate is improved, but error spreading at decoder becomes serious
Solution Approach 1:
The image is divided into multiple blocks, and each block is independently encoded using polar codes. This segmentation isolates errors to specific blocks, preventing error spreading across the entire image, while maintaining high compression rates through efficient polar code encoding of each segment.
Solution Approach 2:
Polar codes serve as an intermediary encoding scheme between the image data and the compressed output. The polar code encoder transforms the original image blocks into a compressed representation that maintains error isolation properties, acting as a mediator that achieves both compression and error control.
2Reliability
If polar coding is used for image compression, then error spreading is controlled, but implementation complexity increases
Solution Approach 1:
By dividing the image into independent blocks for polar code encoding, the overall implementation complexity is reduced. Each block can be processed separately with identical algorithms, allowing for modular implementation and reuse of encoding/decoding modules, thus managing complexity while maintaining error control.
Solution Approach 2:
The patent adjusts parameters of the polar code encoding process, such as block size and code rate, to optimize the balance between error control performance and implementation complexity. By tuning these parameters, the system achieves effective error spreading control without excessive computational burden.
3Ease of operation
If fixed-length entropy coding is implemented, then coding simplicity is improved, but compression efficiency decreases
Solution Approach 1:
The image is segmented into blocks that are independently encoded with fixed-length codes. This segmentation allows the system to use simple fixed-length encoding for each block while the collective effect across all blocks achieves reasonable compression efficiency, balancing simplicity and performance.
Solution Approach 2:
The patent optimizes parameters such as block size and code allocation to improve the efficiency of fixed-length entropy coding. By carefully selecting these parameters, the system enhances compression efficiency while maintaining the simplicity of fixed-length coding schemes.
Data Source
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AI summary
This application discloses an image signal processing method and apparatus, and relates to the field of image processing. The method and the apparatus are easy to implement, have a low compression rate, and effectively control error spreading at a decoder, to improve image compression performance. The specific solution is: obtaining a binary source XN of an image; performing a polarization operation on XN, to obtain a to-be-compressed bit set UN; obtaining a compressed reserved bit set US from UN; performing channel coding on US, to obtain a coded bit sequence; and outputting the coded bit sequence.