Pattern recognition with moment invariants: a comparative study and new results
Pattern Recognition
Moment-based texture segmentation
Pattern Recognition Letters
What's wrong with mean-squared error?
Digital images and human vision
Signal Processing - Special issue on image and video quality metrics
Content-partitioned structural similarity index for image quality assessment
Image Communication
Image quality assessment by discrete orthogonal moments
Pattern Recognition
The effects of a visual fidelity criterion of the encoding of images
IEEE Transactions on Information Theory
Image analysis by Tchebichef moments
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Some computational aspects of discrete orthonormal moments
IEEE Transactions on Image Processing
An information fidelity criterion for image quality assessment using natural scene statistics
IEEE Transactions on Image Processing
Image information and visual quality
IEEE Transactions on Image Processing
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
IEEE Transactions on Image Processing
Discriminant analysis and similarity measure
Pattern Recognition
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In this paper, the similarity of moment vectors between the test and the reference image blocks together with the result from the block classification are used in the formulation of an image quality metric (IQM). First, the reference and the test images are divided into non-overlapping 8x8 blocks and transformed into moment domain using Discrete Tchebichef Transform. The moment features are then used in two operations: the local quality index calculation and the image content (block) classification. The local quality index is obtained from the similarity measure of moment vectors between the reference and the test image blocks. Next, the content of each reference image block is classified into three types: ''plain'', ''edge'' and ''texture'', based on its moment energy level and moment energy distribution. The local quality indices obtained from all the image blocks are then averaged based on the block types to obtain three mean quality scores for each test image. The performance of these three mean quality scores and their combinations are studied using the LIVE database. The results show that the performance of the metric is significantly improved by combining the mean quality scores from the edge and texture image region. The best combination (the proposed metric) is then compared with five other IQMs using the LIVE database and four other independent databases. The results show that the proposed metric performs comparatively well for all the databases.