Entropy Coding with Embedded DCT Statistics for Lower Memory Use
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Solution Overview
Problem
Existing entropy coding methods in image compression, such as JPEG, require additional memory to store DCT coefficient statistics, leading to increased data size and computational inefficiencies due to expensive operations like determining zero coefficients.
Innovation Solution
Storing image statistics, like bit-length and zero-run information, within the available bits of DCT coefficients themselves, allowing for intelligent access and reduced redundant computations during entropy coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If additional memory is used to store DCT coefficient statistics, then computational efficiency is improved, but memory usage increases beyond the input image size
Solution Approach 1:
The patent merges the storage of DCT coefficient statistics with the DCT coefficients themselves by utilizing unused bits within the coefficient data structure. This integration eliminates the need for separate memory allocation for statistics, thereby improving computational efficiency without increasing overall memory usage.
Solution Approach 2:
The DCT coefficient data structure is made multi-functional by using its available bits to serve dual purposes: representing the actual coefficient values and storing statistical information simultaneously. This universal usage of the same data structure resolves the contradiction between computational efficiency and memory usage.
2Manufacturing precision
If computational operations for finding bit-length and checking zero coefficients are performed, then encoding accuracy is maintained, but computational cost increases
Solution Approach 1:
The patent performs preliminary computation by calculating and storing bit-length information and zero-coefficient status during the DCT coefficient generation phase. This preliminary action ensures encoding accuracy is maintained while reducing the computational cost during the actual entropy coding process, as these values are readily available from prior computations.
Solution Approach 2:
The DCT coefficient structure serves itself by containing embedded statistical information that it can provide for its own encoding process. The coefficients self-document their bit-length and zero-status, eliminating the need for separate computational checks and reducing overall computational cost while maintaining accuracy.
Data Source
AI summary
An optimized method and system for entropy coding is described. The system may include an input module to receive a digital image, a Discrete Cosine Transform (DCT) module to obtain a plurality of DCT coefficients associated with the digital image, a statistics generator to generate statistics associated with the plurality of DCT coefficients, a placement module to store the generated statistics in the plurality of DCT coefficients and entropy coder to generate uniquely decodable (UD) codes for the plurality of DCT coefficients. The entropy coder may utilize the statistics stored in the plurality of DCT coefficients.