Image Quantization Partitioning for Compression Efficiency
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Solution Overview
Problem
Conventional image compression methods fail to efficiently compress digital data due to uniform quantization of all samples within a coding block, leading to suboptimal compression results, especially in scenarios where samples exhibit varying properties such as differing frequencies.
Innovation Solution
An image-processing apparatus and method that partitions input images into blocks and selects multiple quantization parameters (QPs) based on defined criteria, such as color components and frequency distributions, to apply sequential encoding schemes like transform coding, quantization coding, and entropy coding, allowing for efficient compression by varying QP values within each block.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform quantization is applied to all samples in a coding block, then the quantization process is simple and fast, but compression efficiency deteriorates because samples with different frequency characteristics are treated equally
Solution Approach 1:
The coding block is divided into multiple sub-blocks based on frequency characteristics. High-frequency samples are separated from low-frequency samples, allowing different quantization parameters to be applied to each sub-block. This segmentation enables optimized compression for different frequency regions while maintaining manageable complexity through automated frequency analysis.
Solution Approach 2:
Different quantization parameters are assigned to different sub-blocks based on their frequency characteristics. High-frequency sub-blocks receive appropriate quantization settings optimized for their content, while low-frequency sub-blocks receive settings optimized for their characteristics. This local optimization improves overall compression efficiency without requiring complete redesign of the quantization process.
2Productivity
If multiple quantization parameters are applied to different sub-blocks, then compression efficiency improves by optimizing for different frequency characteristics, but processing complexity increases due to additional analysis and parameter selection steps
Solution Approach 1:
Frequency characteristics of samples are analyzed and sub-blocks are identified before the quantization process begins. This preliminary frequency analysis allows the system to prepare quantization parameters in advance for different regions, enabling efficient parallel processing during compression and reducing overall processing time despite the added complexity of multi-parameter quantization.
Data Source
AI summary
An image-processing apparatus and method for quantization partitioning for enhanced image compression, includes storage of an input image in a first storage space having a first storage access bandwidth. The image-processing apparatus selects a plurality of QP values from a defined QP range for a first block of a plurality of blocks of the input image. The plurality of QP values are selected from the defined QP range based on defined criteria. The image-processing apparatus is configured to encode, by the selected plurality of QP values, the first block to generate an encoded bit stream of the first block. The encoded bit stream of the first block is storable in a reduced second storage space in the memory with a reduced second storage access bandwidth.