A Comparison of the Statistical Properties of IQA Databases Relative to a Set of Newly Captured High-Definition Images

A broad range of image processing applications require image databases during development and testing. Whilst some image databases have been assembled with specific applications in mind, others are intended for more general use, with image content that is purposefully not application-specific. General-purpose image databases are in frequent use in the development of new compression algorithms, including in the evaluation of the efficacy of lossy compression techniques via statistical and human (perceptual) image quality assessment methods. The question of how the images featuring in standard image databases are selected is important, but is rarely quantitatively justified. In this article, we describe the compilation of a new image database of high-definition color images. We present statistical analyzes both of the images that feature in the most widely used extant databases, and the new database that we have compiled, in order to evaluate how broad a range of the statistics measured each database spans.

[1]  Alan C. Bovik,et al.  A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms , 2006, IEEE Transactions on Image Processing.

[2]  J. H. Hateren,et al.  Independent component filters of natural images compared with simple cells in primary visual cortex , 1998 .

[3]  Nuria Aleixos,et al.  Erratum to: Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables , 2011 .

[4]  Jerry D. Gibson,et al.  Handbook of Image and Video Processing , 2000 .

[5]  J. Gómez-Sanchís,et al.  Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables , 2011 .

[6]  D J Field,et al.  Local Contrast in Natural Images: Normalisation and Coding Efficiency , 2000, Perception.

[7]  Tubagus Maulana Kusuma,et al.  Reduced-reference metric design for objective perceptual quality assessment in wireless imaging , 2009, Signal Process. Image Commun..

[8]  Alan C. Bovik,et al.  GAFFE: A Gaze-Attentive Fixation Finding Engine , 2008, IEEE Transactions on Image Processing.

[9]  Jeffrey Scott Vitter,et al.  Random sampling with a reservoir , 1985, TOMS.

[10]  Umesh Rajashekar,et al.  DOVES: a database of visual eye movements. , 2009, Spatial vision.

[11]  Alan C. Bovik,et al.  Image quality assessment using natural scene statistics , 2004 .

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

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

[14]  Andriana Olmos,et al.  A biologically inspired algorithm for the recovery of shading and reflectance images , 2004 .

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

[16]  José Blasco,et al.  Citrus sorting by identification of the most common defects using multispectral computer vision , 2007 .

[17]  Patrick Le Callet,et al.  Pseudo no reference image quality metric using perceptual data hiding , 2006, Electronic Imaging.

[18]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[19]  Antonio Torralba,et al.  Statistics of natural image categories , 2003, Network.

[20]  David Mumford,et al.  Statistics of natural images and models , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[21]  J. V. van Hateren,et al.  Independent component filters of natural images compared with simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[22]  Erik Reinhard,et al.  Image Statistics and their Applications in Computer Graphics , 2010, Eurographics.

[23]  Yuukou Horita,et al.  Impact of subjective dataset on the performance of image quality metrics , 2008, 2008 15th IEEE International Conference on Image Processing.

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

[25]  T Troscianko,et al.  Color and luminance information in natural scenes. , 1998, Journal of the Optical Society of America. A, Optics, image science, and vision.

[26]  Yuukou Horita,et al.  No-Reference Image Quality Evaluation Model for JPEG and JPEG2000 Images , 2008 .

[27]  Eero P. Simoncelli.  4.7 – Statistical Modeling of Photographic Images , 2005 .