JPEG 2000 Block Coding with Three Bitstreams for Faster Transcoding
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Solution Overview
Problem
Existing image compression technologies face challenges with computational complexity, especially in hardware implementations, which can become a bottleneck for real-time processing of high-resolution video streams and energy-efficient rendering, particularly in mobile devices and cloud-based video services.
Innovation Solution
The FAST Block Coding Algorithm (FBCOT) is introduced as a drop-in replacement for the J2K-1 block coding algorithm, reducing computational complexity while maintaining compatibility with JPEG 2000 standards, allowing for lossless transcoding and preserving essential features like quality scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If JPEG 2000 block coding algorithm is used, then compression efficiency and quality scalability are maintained, but computational complexity increases significantly
Solution Approach 1:
The patent divides the codeblock processing into multiple passes (cleanup pass, significance propagation pass, magnitude refinement pass) and further segments the processing into parallel work units that can be executed concurrently. This segmentation reduces the computational burden of any single processing unit while maintaining the overall compression efficiency of the JPEG 2000 standard.
Solution Approach 2:
The patent performs preliminary classification of coefficients into significant and insignificant groups before detailed encoding. By pre-identifying significant coefficients and organizing them into separate processing streams, the algorithm reduces the computational complexity of subsequent encoding steps while preserving compression quality.
2Manufacturing precision
If JPEG 2000 block coding algorithm is used, then compression quality is maintained, but processing time increases
Solution Approach 1:
The patent implements dynamic processing where the encoding complexity adapts based on the characteristics of the input data. Different codeblocks can be processed with different levels of detail depending on their content, allowing faster processing of simple regions while maintaining high quality for complex regions, thus reducing overall processing time without sacrificing compression quality.
Solution Approach 2:
The patent applies full JPEG 2000 encoding only to necessary regions while using simplified processing for other areas. This partial application of the full algorithm reduces processing time for images where not all regions require maximum compression quality, while still maintaining acceptable compression quality overall.
3Device complexity
If JPEG 2000 block coding algorithm is used, then compression efficiency is achieved, but hardware implementation becomes bottlenecked
Solution Approach 1:
The patent segments the hardware processing into independent parallel units that can operate simultaneously. Each work unit processes a portion of the codeblock independently, allowing hardware implementations to achieve real-time processing by simply increasing the number of parallel units rather than making each unit more complex.
Solution Approach 2:
The patent designs universal processing units that can handle multiple types of operations (cleanup pass, significance propagation, magnitude refinement) within the same hardware structure. This multi-functionality reduces the overall hardware complexity by reusing the same processing units for different stages of encoding rather than requiring separate dedicated hardware for each operation.
Data Source
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AI summary
A Further Improved Method and Apparatus for Image Compression The present invention relates to an improved method and apparatus for image compression and particularly to an improved block coding apparatus and method for compression for use with the JPEG2000 standard, although not limited to this. Methods for coding and decoding blocks and subbands samples derived from still images video frames or related media, involving three bit-streams and the partitioning of samples from the blocking to define groups, is provided. A first bit-stream encodes the significance of whole groups. A second bit-stream encodes the significance of individual samples within each group. The second bit-stream also encodes an unsigned residual value for each significant group. A third bit stream provides a sign bit and any additional magnitude bits required to represent the significant sample values. Exponent predictors are computal using both exponent bounds and the additional magnitude bits associated with previous samples in the block.