Adaptive Image Compression Using Block-Characteristics Quantization
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Solution Overview
Problem
Traditional image compression methods, such as JPEG, uniformly compress blocks using a single quantization table, failing to adaptively compress blocks based on their characteristics, leading to inefficient compression ratios and potential degradation in image quality.
Innovation Solution
An adaptive image compression method and system that uses a block-characteristics quantization table to adjust the quantization level and discrete cosine transform (DCT) coefficients for each block, allowing for differential compression ratios while maintaining similar image quality, by calculating additional compression values based on specific block characteristics and error limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single quantization table is used for uniform compression of all blocks, then the compression process is simple and fast, but the compression ratio is inefficient and image quality degrades
Solution Approach 1:
The patent segments the image into multiple blocks and applies different quantization strategies to different blocks based on their characteristics. Each block is analyzed to determine if it contains important features, and then appropriate quantization levels are applied selectively, resolving the contradiction between uniform simple processing and differentiated quality-preserving processing.
Solution Approach 2:
The patent implements local quality control by applying different quantization levels to different blocks based on their local characteristics. Blocks containing important features (edges, textures, faces) are processed with higher quality preservation, while uniform regions can be compressed more aggressively, thus maintaining overall image quality while improving compression efficiency.
2Device complexity
If a single quantization table is used for all blocks, then the system complexity is low, but the compression ratio is poor
Solution Approach 1:
The patent introduces dynamic adaptation by adjusting quantization parameters based on block characteristics. The system dynamically determines which blocks require higher quality preservation and applies appropriate quantization levels, transforming the static single-quantization-table approach into a dynamic multi-level quantization system that adapts to local image properties.
Solution Approach 2:
The patent changes the quantization parameters selectively for different blocks based on their characteristics. By modifying quantization levels, precision, and quality factors locally rather than globally, the system achieves better compression ratios without requiring complete system redesign, thus managing complexity while improving performance.
3Loss of information
If iterative compression with quality checking is performed, then the compression ratio is optimized, but the processing time increases significantly
Solution Approach 1:
The patent performs preliminary analysis of each block to identify important features (edges, textures, faces) before compression. By pre-determining which blocks require quality preservation based on their characteristics, the system avoids iterative quality checking during compression, thus achieving optimized compression ratios without the time penalty of repeated iterations.
Solution Approach 2:
The system uses block characteristics themselves (edge density, texture patterns, face detection results) to automatically determine appropriate quantization levels without requiring external quality assessment iterations. The blocks essentially self-determine their compression parameters based on their intrinsic properties, eliminating the need for time-consuming iterative quality checking.
Data Source
AI summary
Method and system for adaptive image compression using block characteristics are provided. For adaptive image compression, blocks in an image are quantized and compressed using a quantization table that was used in quantization of the blocks. An image compression system checks a block-characteristics quantization table corresponding to a specific block in the image to be compressed in order to quantize the specific block, and performs calculation using a value of a first element of the quantization table corresponding to a specific element of the specific block and a value of a second element of the checked block-characteristics quantization table corresponding to the specific element in order to quantize the specific element.