Color image database TID2013: Peculiarities and preliminary results

Visual quality of color images is an important aspect in various applications of digital image processing and multimedia. A large number of visual quality metrics (indices) has been proposed recently. In order to assess their reliability, several databases of color images with various sets of distortions have been exploited. Here we present a new database called TID2013 that contains a larger number of images. Compared to its predecessor TID2008, seven new types and one more level of distortions are included. The need for considering these new types of distortions is briefly described. Besides, preliminary results of experiments with a large number of volunteers for determining the mean opinion score (MOS) are presented. Spearman and Kendall rank order correlation factors between MOS and a set of popular metrics are calculated and presented. Their analysis shows that adequateness of the existing metrics is worth improving. Special attention is to be paid to accounting for color information and observers focus of attention to locally active areas in images.

[1]  Damon M. Chandler,et al.  ${\bf S}_{3}$: A Spectral and Spatial Measure of Local Perceived Sharpness in Natural Images , 2012, IEEE Transactions on Image Processing.

[2]  David Zhang,et al.  A comprehensive evaluation of full reference image quality assessment algorithms , 2012, 2012 19th IEEE International Conference on Image Processing.

[3]  Shao-Yi Chien,et al.  Combination of SSIM and JND with content-transition classification for image quality assessment , 2012, 2012 Visual Communications and Image Processing.

[4]  M. Kendall,et al.  The advanced theory of statistics , 1945 .

[5]  Jon Y. Hardeberg,et al.  Attributes of image quality for color prints , 2010, J. Electronic Imaging.

[6]  Zhou Wang,et al.  Information Content Weighting for Perceptual Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[7]  Sheila S. Hemami,et al.  VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images , 2007, IEEE Transactions on Image Processing.

[8]  R. Fisher The Advanced Theory of Statistics , 1943, Nature.

[9]  Eric C. Larson,et al.  Most apparent distortion: full-reference image quality assessment and the role of strategy , 2010, J. Electronic Imaging.

[10]  Weisi Lin,et al.  A multi-metric fusion approach to visual quality assessment , 2011, 2011 Third International Workshop on Quality of Multimedia Experience.

[11]  Nikolay N. Ponomarenko,et al.  Visual quality analysis for images degraded by different types of noise , 2013, Electronic Imaging.

[12]  Nikolay N. Ponomarenko,et al.  TID2008 – A database for evaluation of full-reference visual quality assessment metrics , 2004 .

[13]  Nikolay N. Ponomarenko,et al.  ADAPTIVE JPEG LOSSY COMPRESSION OF COLOR IMAGES , 2011 .

[14]  Nikolay N. Ponomarenko,et al.  HVS-metric-based performance analysis of image denoising algorithms , 2011, 3rd European Workshop on Visual Information Processing.

[15]  Weisi Lin,et al.  An Overview of Perceptual Processing for Digital Pictures , 2012, 2012 IEEE International Conference on Multimedia and Expo Workshops.

[16]  Wilson S. Geisler,et al.  Image quality assessment based on a degradation model , 2000, IEEE Trans. Image Process..

[17]  R. M. Willett,et al.  Compressed sensing for practical optical imaging systems: A tutorial , 2011, IEEE Photonics Conference 2012.

[18]  Theophano Mitsa,et al.  Evaluation of contrast sensitivity functions for the formulation of quality measures incorporated in halftoning algorithms , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[19]  MICHAEL W. DAVIDSON,et al.  The FOCAL ENCYCLOPEDIA of Photography, 4th Edition , 2008 .

[20]  Nikolay N. Ponomarenko,et al.  METRICS PERFORMANCE COMPARISON FOR COLOR IMAGE DATABASE , 2008 .

[21]  J. Astola,et al.  ON BETWEEN-COEFFICIENT CONTRAST MASKING OF DCT BASIS FUNCTIONS , 2007 .

[22]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[23]  Krzysztof Okarma,et al.  Colour Image Quality Assessment Using the Combined Full-Reference Metric , 2011, Computer Recognition Systems 4.

[24]  Alan C. Bovik,et al.  Visual quality assessment algorithms: what does the future hold? , 2010, Multimedia Tools and Applications.

[25]  Stefan Winkler,et al.  Analysis of Public Image and Video Databases for Quality Assessment , 2012, IEEE Journal of Selected Topics in Signal Processing.

[26]  Alan C. Bovik,et al.  Image information and visual quality , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[27]  Edward R. Vrscay,et al.  SSIM-inspired image restoration using sparse representation , 2012, EURASIP Journal on Advances in Signal Processing.

[28]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[29]  Zhou Wang,et al.  Multi-scale structural similarity for image quality assessment , 2003 .

[30]  C.-C. Jay Kuo,et al.  Perceptual image quality assessment using block-based multi-metric fusion (BMMF) , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[31]  Marco Carli,et al.  Modified image visual quality metrics for contrast change and mean shift accounting , 2011, 2011 11th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM).

[32]  Doina I. Petrescu,et al.  Quality and noise measurements in mobile phone video capture , 2011, Electronic Imaging.

[33]  Weisi Lin,et al.  Perceptual visual quality metrics: A survey , 2011, J. Vis. Commun. Image Represent..

[34]  Vladimir V. Lukin,et al.  Image Informative Maps for Estimating Noise Standard Deviation and Texture Parameters , 2011, EURASIP J. Adv. Signal Process..

[35]  Mohammed Hassan,et al.  Structural Similarity Measure for Color Images , 2012 .

[36]  Nikolay N. Ponomarenko,et al.  A NEW FULL-REFERENCE QUALITY METRICS BASED ON HVS , 2006 .