Frequency domain blockiness measurement for image quality assessment

Block-based digital image and video compression could lead to visible distortions in the coded image or video, with dominant artifacts like blockiness. Subjective quality assessments [1] are reliable but they are too costly and not possible to be computerized. The objective quality meter is designed for evaluating the image quality. The blockiness is measured in frequency domain in which the ratio of harmonics to other AC components is calculated and used to estimate the blockiness artifact. Before blockiness estimation in frequency domain, the property of Human Visual System is applied in the form of edge cancellation and spatial masking in spatial domain. After the spatial masking of coded image according to the spatial activity of the reference image, the blockiness is calculated locally in frequency domain and accumulated in the end for a single quality metric. The results in the end show that this approach is very simple to implement and has easy calculations. The work is tested on 233 images from LIVE image database and the Pearson's Correlation Coefficient of 94% is obtained even without training on any image.

[1]  Jan P. Allebach,et al.  Measurement of ringing artifacts in JPEG images , 2006, Electronic Imaging.

[2]  Stephane Pefferkorn,et al.  Perceptual quality metric of color quantization errors on still images , 1998, Electronic Imaging.

[3]  Abdelhakim Saadane,et al.  A New Reference Free Approach for the Quality Assessment of MPEG Coded Videos , 2005, ACIVS.

[4]  Jean-Bernard Martens,et al.  A single-ended blockiness measure for JPEG-coded images , 2002, Signal Process..

[5]  Yuukou Horita,et al.  No reference image quality assessment for JPEG2000 based on spatial features , 2008, Signal Process. Image Commun..

[6]  Mohammed Ghanbari,et al.  Frequency domain measurement of blockiness in MPEG-2 coded video , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[7]  H.R. Wu,et al.  A generalized block-edge impairment metric for video coding , 1997, IEEE Signal Processing Letters.

[8]  Zhou Wang,et al.  Blind measurement of blocking artifacts in images , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[9]  Jae Wook Jeon,et al.  No-Reference Image Quality Assessment using Blur and Noise , 2009 .

[10]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

[11]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[12]  Xiang Zhu,et al.  A no-reference sharpness metric sensitive to blur and noise , 2009, 2009 International Workshop on Quality of Multimedia Experience.

[13]  Jeffrey Lubin A human vision system model for objective image fidelity and target detectability measurements , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[14]  Stephen D. Voran,et al.  Objective video quality assessment system based on human perception , 1993, Electronic Imaging.