A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Complex wavelet structural similarity: a new image similarity index
IEEE Transactions on Image Processing
Wavelet steerability and the higher-order Riesz transform
IEEE Transactions on Image Processing
Content-partitioned structural similarity index for image quality assessment
Image Communication
IEEE Transactions on Signal Processing
Gradient information-based image quality metric
IEEE Transactions on Consumer Electronics
Image quality assessment based on a degradation model
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
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
FSIM: A Feature Similarity Index for Image Quality Assessment
IEEE Transactions on Image Processing
A hybrid system for distortion classification and image quality evaluation
Image Communication
MLSIM: A Multi-Level Similarity index for image quality assessment
Image Communication
MLSIM: A Multi-Level Similarity index for image quality assessment
Image Communication
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Image quality assessment (IQA) is of great importance to numerous image processing applications, and various methods have been proposed for it. In this paper, a Multi-Level Similarity (MLSIM) index for full reference IQA is proposed. The proposed metric is based on the fact that human visual system (HVS) distinguishes the quality of an image mainly according to the details given by low-level gradient information. In the proposed metric, the Prewitt operator is first utilized to get gradient information of both reference and distorted images, then the gradient information of reference image is segmented into three levels (3LSIM) or two levels (2LSIM), and the gradient information of distorted image is segmented by the corresponding regions of reference image, therefore we get multi-level information of these two images. Riesz transform is utilized to get corresponding features of different levels and the corresponding 1st-order and 2nd-order coefficients are combined together by regional mutual information (RMI) and weighted to obtain a single quality score. Experimental results demonstrate that the proposed metric is highly consistent with human subjective evaluations and achieves good performance.