Bitplane Serialization for Fast Image Compression
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Solution Overview
Problem
Current data compression techniques for large images, such as those in digital pathology, face challenges in achieving a dynamic tradeoff between bandwidth use and image quality, with existing methods like JPEG2000 being slow and requiring specialized hardware for real-time compression.
Innovation Solution
A method involving the serialization of bitplanes, run-length encoding of 0 bit sequences, and optimal prefix encoding of bits based on local context, allowing for fast and efficient compression, with the option to progressively decompress images by omitting least significant bitplanes for quicker review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If JPEG2000 wavelet transform encoding is used, then image quality is improved with less distortion and noise, but compression speed becomes slow and real-time compression requires specialized hardware
Solution Approach 1:
The patent segments the image data into bitplanes and further into subbands through wavelet transform, then applies different encoding strategies to each segment. The bitplane segmentation allows progressive decoding where only necessary bitplanes need to be processed for quick review, while the wavelet subband division enables frequency-based compression optimization.
Solution Approach 2:
The patent implements partial decoding by allowing users to decode only the necessary bitplanes for quick review without processing all bitplanes for full quality reconstruction. This partial action approach enables fast compression and decoding when high quality is not required, while maintaining the option for full quality decoding when needed.
2Loss of information
If complex bitplane coding such as EBCOT is used, then compression efficiency is improved, but processing time increases significantly
Solution Approach 1:
The patent changes the encoding parameters by using simplified run-length encoding and context-based prefix coding instead of complex adaptive arithmetic coding. The context model uses a predetermined number of neighboring bits to predict current bit values, which is less computationally intensive than full EBCOT but achieves comparable compression efficiency.
Solution Approach 2:
The patent uses a simpler, less expensive encoding algorithm that can be quickly executed without requiring complex hardware implementations. The context-based prefix coding and run-length encoding provide sufficient compression with much lower computational complexity than EBCOT, enabling real-time processing on standard hardware.
3Manufacturing precision
If full image compression is performed, then image quality is maintained, but decompression time increases preventing real-time review
Solution Approach 1:
The patent segments image data into multiple bitplanes ordered by significance, allowing decompression to start from most significant bitplanes and proceed to less significant ones. This segmentation enables progressive reconstruction where the image becomes progressively clearer as more bitplanes are decoded, allowing real-time review at varying quality levels.
Solution Approach 2:
The patent enables partial decoding by allowing users to stop decoding after processing only the necessary number of bitplanes for their review needs. This partial action approach provides fast decompression for quick review while maintaining the option to decode all bitplanes for full quality when time permits.
Data Source
AI summary
The invention relates to a method and apparatus for fast and efficient image compression and decompression comprising transform coding of image data to generate an image representation using transform coefficients, bit-plane serialization of the image representation using transform coefficients, and for each bitplane optimal prefix encoding of bits in a bitplane sharing local context, run-length encoding of 0 bit sequences in the bitplane and storing the coefficients received after optimal prefix encoding and 0 run length encoding starting with the sign followed by the bits in the order of significance, starting with the most significant bit to the least significant bit in a seektable in a header section.