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Image Information and Visual Quality
H. R. Sheikh, and A. C. Bovik
IEEE Transactions on Image Processing
Keywords: Image Quality Assessment, Natural Scene Statistics, Image Information, Information Fidelity.
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. Image QA algorithms generally 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 modelling salient physiological and psychovisual
features of the human visual system (HVS), or by signal fidelity measures. In this paper we approach the image
QA problem as an information fidelity problem. Specifically we propose to quantify the loss of image information
to the distortion process, and explore the relationship between image information and visual quality. QA systems
are invariably involved with judging the visual quality of ‘natural’ images and videos that are meant for ‘human
consumption.’ Researchers have developed sophisticated models to capture the statistics of such natural signals. Using
these models, we previously presented an information fidelity criterion for image quality assessment that related image
quality with the amount of information shared between a reference and a distorted image. In this paper, we propose
an image information measure that quantifies the information that is present in the reference image, and also quantify
how much of this reference information can be extracted from the distorted image. Combining these two quantities,
we propose a visual information fidelity measure for image quality assessment. We validate the performance of our
algorithm with an extensive subjective study involving 779 images, and show that our method outperforms recent
state-of-the-art image quality assessment algorithms by a sizeable margin in our simulations. The code and the data
from the subjective study are available at [1].
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