Variable Decomposition Image Quality Assessment
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Solution Overview
Problem
Current image quality assessment methods are limited by high computational complexity, difficulty in accurately determining quality metrics, and inefficiencies in reflecting human visual system sensitivity, particularly in decomposing images into multiple resolutions and combining bands into a final metric.
Innovation Solution
A method and system that apply a variable number of multi-level decompositions to determine image quality by processing images through N-level multiresolution decomposition, generating approximation and edge maps, and using specific quality metrics like SSIM, AD, or PSNR to calculate a quality score, optimizing the number of decomposition levels based on viewing distance and image resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed high number of decomposition levels is applied to all images, then measurement precision may improve, but device complexity and computational load increase significantly
Solution Approach 1:
The patent applies a dynamic number of decomposition levels instead of a fixed number. The system adjusts the decomposition level N based on image characteristics (such as complexity, content type, and resolution) to optimize the balance between assessment accuracy and computational efficiency. Different images receive different numbers of decomposition levels, making the system adaptive rather than static.
Solution Approach 2:
The patent changes the parameter of decomposition level N dynamically based on image properties. By analyzing image characteristics and adjusting N accordingly, the system achieves high measurement precision for complex images while reducing computational load for simpler images, effectively resolving the contradiction between accuracy and complexity.
2Measurement precision
If more decomposition levels are applied, then quality metric accuracy improves, but processing time increases
Solution Approach 1:
The system dynamically determines the number of decomposition levels N based on image characteristics and quality assessment requirements. For images requiring high precision assessment, more decomposition levels are applied. For real-time or less critical applications, fewer levels are used, reducing processing time while maintaining acceptable accuracy.
Solution Approach 2:
The patent applies partial decomposition levels based on the actual needs of each image. Instead of always applying the maximum number of decomposition levels, the system uses only the necessary number of levels required to achieve the desired assessment quality, avoiding excessive processing for simple images.
3Ease of operation
If uniform decomposition is applied to all images regardless of content, then ease of operation is maintained, but measurement precision decreases for specific image types
Solution Approach 1:
The patent applies different decomposition strategies to different regions or types of images based on their specific characteristics. Instead of uniform decomposition, the system analyzes image content and applies appropriate decomposition levels locally, achieving high precision for specific image types while maintaining reasonable operational simplicity through automated detection.
Solution Approach 2:
The system performs self-analysis of image characteristics and automatically determines the appropriate number of decomposition levels without requiring manual intervention. This maintains ease of operation while achieving precision tailored to each image type, as the system serves itself by making intelligent decisions based on image content.
Data Source
AI summary
Method and system for determining a measure of quality for images are presented. Multi-level decomposition of images in the wavelet domain using a variable number of levels of decomposition and aggregation of selected subbands is performed to obtain an accurate measure of quality. The processing time is reduced in comparison to that required by other methods for generating measures of quality.