Image Quality Control via Dynamic Quantization and Truncation
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Solution Overview
Problem
The JPEG2000 image compression standard lacks specific guidance on encoding decisions such as rate allocation, quantization binwidths, and truncation, leading to distortion in compressed images due to implementation precision, quantization, and codestream truncation, which affect image quality.
Innovation Solution
A technique for controlling image quality by selecting target quality metrics, estimating and measuring distortion during compression, adjusting quantization tables and truncation points to ensure compressed images meet specified quality metrics, and applying these methods to various image types and sequences, including video and 3D data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression is applied to reduce file size, then compression efficiency is improved, but image quality deteriorates due to distortion
Solution Approach 1:
The patent implements quality control through feedback mechanisms where the encoder estimates distortion metrics (such as PSNR or SSIM) and uses this information to adjust compression parameters. The system continuously monitors image quality and modifies quantization binwidths and truncation points based on actual quality measurements, creating a closed-loop control system that balances compression efficiency with quality preservation.
Solution Approach 2:
The patent dynamically adjusts compression parameters including quantization binwidths, truncation points, and wavelet transform levels based on quality metrics. By changing these parameters adaptively during the compression process, the system optimizes the trade-off between file size reduction and image quality maintenance, ensuring that distortion remains within acceptable thresholds while achieving desired compression ratios.
2Productivity
If quantization is applied to reduce data precision, then compression ratio is improved, but distortion is introduced
Solution Approach 1:
The patent employs dynamic quantization where quantization binwidths are not fixed but adapt based on image content and quality requirements. The system adjusts quantization strength locally and globally, using quality control feedback to modulate the quantization process in real-time. This dynamic approach allows aggressive quantization in regions where quality loss is less perceptible while preserving detail in critical areas.
Solution Approach 2:
The patent applies different quantization strategies to different regions of the image based on local quality requirements. By analyzing image characteristics and applying quality control metrics locally, the system determines optimal quantization binwidths for each region, allowing higher compression in low-importance areas while maintaining higher precision in critical regions.
3Quantity of substance
If codestream truncation is applied to limit data size, then compressed file size is reduced, but quality metric is compromised
Solution Approach 1:
The patent performs preliminary estimation of distortion metrics and quality control calculations before final truncation decisions are made. By pre-calculating the impact of potential truncation points and estimating resulting quality metrics, the system can make informed decisions about optimal truncation locations that minimize quality loss while achieving target file sizes. This preliminary action allows for more precise control over the truncation process.
Data Source
AI summary
A technique for controlling the quality of one or more compressed images. The technique allows, for example, the selection of a target quality metric(s) and the compression of the image(s) such the compressed image(s) meets the metric(s). Alternatively, a target quality metric can be specified, and the image(s) compressed using parameters estimated to achieve the target quality. Optionally, the quality metric can also be made available to, for example, a user on an image processing system. The quality metrics can be, for example, for one or more layers, one or more images and/or one or more image sequences.