How to Learn the Effect of Non-Uniform Distortion on Perceived Visual Quality? Case Study Using Convolutional Sparse Coding for Quality Assessment of Synthesized Views

Machine learning has been attached greater attention in the field of quality assessment and related models have been developed by assuming that any region within images share the same quality score. However, this assumption may no longer stand when non-uniform distortions exist, e.g. geometric disto...

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Bibliographic Details
Published in2018 25th IEEE International Conference on Image Processing (ICIP) pp. 286 - 290
Main Authors Ling, Suiyi, Callet, Patrick Le
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
Convolution
Convolutional codes
Convolutional Sparse Coding
Distortion
Feature extraction
FTV
Kernel
Measurement
No reference Quality assessment
Synthesized views
Training
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Summary:Machine learning has been attached greater attention in the field of quality assessment and related models have been developed by assuming that any region within images share the same quality score. However, this assumption may no longer stand when non-uniform distortions exist, e.g. geometric distortion within synthesized views in the case of free- view point video TV (FTV). In this paper, we explore to use Convolutional Sparse Coding (CSC), which computes a sparse representation for an entire image with the sum of a set of convolutions with dictionary filters instead of a linear combination of a set of dictionary atoms, to learn a visible codebook and propose a methodology to quantify the geometric distortion without using the reference. Experimental results show that the proposed no reference metric is the most visual friendly and reliable metric among the compared blind image metrics designed for synthesized images.
ISSN:2381-8549
DOI:10.1109/ICIP.2018.8451151