An information fidelity criterion for image quality assessment using natural scene statistics
Top Cited Papers
- 14 November 2005
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 14 (12), 2117-2128
- https://doi.org/10.1109/tip.2005.859389
Abstract
Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Traditionally, image QA algorithms interpret image quality as fidelity or similarity with a "reference" or "perfect" image in some perceptual space. Such "full-reference" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by arbitrary signal fidelity criteria. In this paper, we approach the problem of image QA by proposing a novel information fidelity criterion that is based on natural scene statistics. QA systems are invariably involved with judging the visual quality of images and videos that are meant for "human consumption". Researchers have developed sophisticated models to capture the statistics of natural signals, that is, pictures and videos of the visual environment. Using these statistical models in an information-theoretic setting, we derive a novel QA algorithm that provides clear advantages over the traditional approaches. In particular, it is parameterless and outperforms current methods in our testing. We validate the performance of our algorithm with an extensive subjective study involving 779 images. We also show that, although our approach distinctly departs from traditional HVS-based methods, it is functionally similar to them under certain conditions, yet it outperforms them due to improved modeling. The code and the data from the subjective study are available at [1].Keywords
This publication has 30 references indexed in Scilit:
- No-reference quality assessment using natural scene statistics: JPEG2000IEEE Transactions on Image Processing, 2005
- The steerable pyramid: a flexible architecture for multi-scale derivative computationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Elements of Information TheoryPublished by Wiley ,2001
- Multiscale image segmentation using wavelet-domain hidden Markov modelsIEEE Transactions on Image Processing, 2001
- Image denoising using a local Gaussian scale mixture model in the wavelet domainPublished by SPIE-Intl Soc Optical Eng ,2000
- Image quality assessment based on a degradation modelIEEE Transactions on Image Processing, 2000
- A Haar Wavelet Approach to Compressed Image Quality MeasurementJournal of Visual Communication and Image Representation, 2000
- A Parametric Texture Model Based on Joint Statistics of Complex Wavelet CoefficientsInternational Journal of Computer Vision, 2000
- Modeling the joint statistics of images in the wavelet domainPublished by SPIE-Intl Soc Optical Eng ,1999
- Quality asessment of coded images using numerical category scalingPublished by SPIE-Intl Soc Optical Eng ,1995