Image Encoding Slice Segmentation for Error Resilience
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Solution Overview
Problem
Existing image encoding methods struggle to maintain high image quality while reducing data requirements for efficient wireless transmission, especially in environments prone to data loss or corruption.
Innovation Solution
A method involving image segmentation into uniform blocks, application of a frequency-based transform, grouping of coefficients into sub-bands, and binary arithmetic coding, with an end-of-slice codeword for error resilience and bit allocation across slices to limit error propagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If compression algorithms are applied to reduce data requirements, then transmission speed improves, but error resilience deteriorates
Solution Approach 1:
The image is divided into multiple slices, and each slice is independently encoded with its own end-of-slice codeword. This segmentation limits error propagation to individual slices rather than affecting the entire image, thereby maintaining reliability while enabling efficient compression of each segment.
Solution Approach 2:
End-of-slice codewords are inserted at predetermined positions before transmission occurs. These codewords serve as synchronization markers that enable the decoder to resynchronize if errors occur, proactively preparing for potential transmission issues while maintaining compression efficiency.
2Reliability
If synchronization codes are added to enable error recovery, then reliability improves, but data compression ratio deteriorates
Solution Approach 1:
Instead of adding comprehensive synchronization codes throughout the entire image, end-of-slice codewords are strategically placed only at the end of each slice. This partial approach provides sufficient error recovery capability while minimizing the additional data volume required.
Solution Approach 2:
The codeword structure is optimized to convey slice termination information efficiently. By changing the parameter placement of synchronization markers to coincide with natural slice boundaries, the system achieves reliable error recovery without excessive data overhead.
3Quantity of substance
If arithmetic coding is applied to compress coefficients, then compression ratio improves, but error propagation risk increases
Solution Approach 1:
Arithmetic coding is applied independently to each slice rather than to the entire image. This segmentation ensures that errors in one slice do not propagate to other slices, maintaining reliability while achieving high compression ratios within each encoded segment.
Solution Approach 2:
The end-of-slice codeword is extracted and placed at the boundary of each slice before arithmetic coding is applied to the remaining coefficients. This extraction creates a natural error boundary that prevents propagation while preserving the compression benefits of arithmetic coding within each slice.
Data Source
AI summary
A method for encoding data defining an image is disclosed. The image is segmented into image blocks, each having a uniform block size. A frequency-based transform is applied to each providing transformed image data in which the image data is represented as coefficients defining a linear combination of predetermined basis functions having different spatial frequencies. Each block of transformed image data comprises one coefficient for a zero frequency basis function, and a plurality of coefficients for higher frequency basis functions which are grouped into one or more sub-bands. The blocks of transformed image data are grouped into slices. The coefficients of a first sub-band of each block in a slice are concatenated, and converted into binary code using binary arithmetic coding, and an end-of-slice codeword is inserted at the end of the sub-band. The concatenation/conversion is repeated for all the sub-bands in the slice, and then for all slices.