Dynamic Image Compression via Buffer-Aware DCT Coefficient Modification
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Solution Overview
Problem
Existing image compression methods for portable devices require multiple passes through image data to meet bit budget constraints, which is not optimal for applications with limited memory and processing resources.
Innovation Solution
A method that dynamically adjusts image compression by generating quantized frequency domain vectors and modifying them based on the current input capacity of a buffer, allowing for efficient serial processing and varying compression levels across image regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple passes through image data are used to meet bit budget constraints, then compression quality is improved, but processing time and memory usage increase
Solution Approach 1:
The patent applies preliminary action by performing a first pass through the DCT coefficient data to determine bit number savings for each incremental reduction of the cutoff ordinal number, before performing the second pass to actually set coefficients to zero. This preliminary analysis enables optimized single-pass compression in subsequent operations.
Solution Approach 2:
The patent implements dynamics by making the compression process adaptive through dynamic adjustment of the cutoff ordinal number based on bit budget constraints. The system dynamically determines which coefficients to zero out by comparing bit savings against the required bit budget, allowing flexible adaptation to different compression requirements.
2Manufacturing precision
If multiple passes through image data are used to meet bit budget constraints, then compression quality is improved, but memory and processing resources increase
Solution Approach 1:
The patent applies preliminary action by performing a first pass through the DCT coefficient data to determine bit number savings for each incremental reduction of the cutoff ordinal number, before performing the second pass to actually set coefficients to zero. This preliminary analysis enables optimized single-pass compression in subsequent operations.
Solution Approach 2:
The system performs self-service by automatically determining the optimal cutoff ordinal number through the first pass analysis, eliminating the need for external intervention or complex multi-pass processing. The compression algorithm self-adjusts to meet bit budget constraints using the pre-computed bit savings information.
3Quantity of substance
If compression level is increased to reduce image size, then storage efficiency is improved, but image quality deteriorates
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different DCT coefficients based on their frequency characteristics and bit savings potential. Instead of uniform compression, the system selectively zeros out coefficients at optimal positions determined by the first pass analysis, preserving quality in critical regions while achieving compression in less important areas.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting the cutoff ordinal number parameter to control the trade-off between compression ratio and image quality. The system changes this parameter based on bit budget requirements, enabling flexible adaptation of compression level to achieve optimal balance between file size and quality.
Data Source
AI summary
Methods, systems, and computer programs for encoding images are described. In one aspect, quantized frequency domain vectors are sequentially generated from a sequence of blocks of the image. Each quantized frequency domain vector includes a set of quantized forward transform coefficients that are derived from a respective image block. For each successive quantized frequency domain vector, a current input capacity level of a buffer is determined and the quantized frequency domain vector is modified to increase compressibility when the current input capacity level is determined to be below a prescribed threshold. Modified and unmodified quantized frequency domain vectors are encoded into a sequence of encoded image blocks. The sequence of encoded image blocks is stored in the buffer.