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MODELING SPARSE SPATIO-TEMPORAL REPRESENTATIONS FOR NO-REFERENCE VIDEO QUALITY ASSESSMENT

Abstract: 

We present a novel No-Reference (NR) video quality assessment (VQA) algorithm that operates on the sparse represent- ation coefficients of local spatio-temporal (video) volumes. Our work is motivated by the observation that the primary visual cortex adopts a sparse coding strategy to represent visual stimulus. We use the popular K-SVD algorithm to construct spatio-temporal dictionary to sparsely represent local spatio-temporal volumes of natural videos. We empirically demonstrate that the histogram of the sparse representations corresponding to each atom in the dictionary can be well modelled using a Generalised Gaussian Distribution (GGD). We then show that the GGD model parameters are good feature for distortion estimation. This, in turn leads us to the proposed NR-VQA algorithm. The GGD model parameters corresponding to each atom of the dictionary form the feature vector that is used to predict quality using Support Vector Regression (SVR). The proposed algorithm delivers competitive performance over the LIVE VQA (SD), EPFL (SD) and the LIVE Mobile high definition (HD) databases. Our algorithm is called SParsity based Objective VIdeo Quality Evaluator (SPOVIQE). The proposed algorithm is simple and computationally efficient as compared with other state-of-the-art NR-VQA algorithms.

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Paper Details

Authors:
Muhammed Shabeer P,. Saurabhchand Bhati, Sumohana S. Channappayya
Submitted On:
12 November 2017 - 9:24am
Short Link:
Type:
Presentation Slides
Event:
Paper Code:
SSP-O.5.1
Document Year:
2017
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Slides for paper 1590 GlobalSIP 2017

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[1] Muhammed Shabeer P,. Saurabhchand Bhati, Sumohana S. Channappayya, "MODELING SPARSE SPATIO-TEMPORAL REPRESENTATIONS FOR NO-REFERENCE VIDEO QUALITY ASSESSMENT", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2315. Accessed: Oct. 16, 2018.
@article{2315-17,
url = {http://sigport.org/2315},
author = {Muhammed Shabeer P;. Saurabhchand Bhati; Sumohana S. Channappayya },
publisher = {IEEE SigPort},
title = {MODELING SPARSE SPATIO-TEMPORAL REPRESENTATIONS FOR NO-REFERENCE VIDEO QUALITY ASSESSMENT},
year = {2017} }
TY - EJOUR
T1 - MODELING SPARSE SPATIO-TEMPORAL REPRESENTATIONS FOR NO-REFERENCE VIDEO QUALITY ASSESSMENT
AU - Muhammed Shabeer P;. Saurabhchand Bhati; Sumohana S. Channappayya
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2315
ER -
Muhammed Shabeer P,. Saurabhchand Bhati, Sumohana S. Channappayya. (2017). MODELING SPARSE SPATIO-TEMPORAL REPRESENTATIONS FOR NO-REFERENCE VIDEO QUALITY ASSESSMENT. IEEE SigPort. http://sigport.org/2315
Muhammed Shabeer P,. Saurabhchand Bhati, Sumohana S. Channappayya, 2017. MODELING SPARSE SPATIO-TEMPORAL REPRESENTATIONS FOR NO-REFERENCE VIDEO QUALITY ASSESSMENT. Available at: http://sigport.org/2315.
Muhammed Shabeer P,. Saurabhchand Bhati, Sumohana S. Channappayya. (2017). "MODELING SPARSE SPATIO-TEMPORAL REPRESENTATIONS FOR NO-REFERENCE VIDEO QUALITY ASSESSMENT." Web.
1. Muhammed Shabeer P,. Saurabhchand Bhati, Sumohana S. Channappayya. MODELING SPARSE SPATIO-TEMPORAL REPRESENTATIONS FOR NO-REFERENCE VIDEO QUALITY ASSESSMENT [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2315