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A detail based method for linear full reference image quality prediction
- Publication Year :
- 2018
- Publisher :
- Institute of Electrical and Electronics Engineers Inc., 2018.
-
Abstract
- In this paper, a novel Full Reference method is proposed for image quality assessment, using the combination of two separate metrics to measure the perceptually distinct impact of detail losses and of spurious details. To this purpose, the gradient of the impaired image is locally decomposed as a predicted version of the original gradient, plus a gradient residual. It is assumed that the detail attenuation identifies the detail loss, whereas the gradient residuals describe the spurious details. It turns out that the perceptual impact of detail losses is roughly linear with the loss of the positional Fisher information, while the perceptual impact of the spurious details is roughly proportional to a logarithmic measure of the signal to residual ratio. The affine combination of these two metrics forms a new index strongly correlated with the empirical Differential Mean Opinion Score (DMOS) for a significant class of image impairments, as verified for three independent popular databases. The method allowed alignment and merging of DMOS data coming from these different databases to a common DMOS scale by affine transformations. Unexpectedly, the DMOS scale setting is possible by the analysis of a single image affected by additive noise.<br />Comment: 15 pages, 9 figures. Copyright notice: The paper has been accepted for publication on the IEEE Trans. on Image Processing on 19/09/2017 and the copyright has been transferred to the IEEE
- Subjects :
- FOS: Computer and information sciences
linear prediction
Scale (ratio)
Image quality
Computer Vision and Pattern Recognition (cs.CV)
Feature extraction
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
Residual
fisher information
full reference image quality assessment
symbols.namesake
Databases
Affine combination
0202 electrical engineering, electronic engineering, information engineering
image gradient
image quality
Spurious relationship
Fisher information
visualization
Mathematics
computer graphics and computer-aided design
linear quality metric
software
feature extraction
020206 networking & telecommunications
VICOM
sensitivity
loss measurement
detail analysis
symbols
020201 artificial intelligence & image processing
Affine transformation
Algorithm
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....2b0fe0e22ec5da189314fbb702648958