Full-Reference Video Quality Assessment Using Deep 3D Convolutional Neural Networks

Dendi, Sathya Veera Reddy and Krishnappa, Gokul and Channappayya, Sumohana (2019) Full-Reference Video Quality Assessment Using Deep 3D Convolutional Neural Networks. In: 25th National Conference on Communications, NCC, 20 - 23 February 2019, Bangalore, India.

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We present a novel framework called Deep Video QUality Evaluator (DeepVQUE) for full-reference video quality assessment (FRVQA) using deep 3D convolutional neural networks (3D ConvNets). DeepVQUE is a complementary framework to traditional handcrafted feature based methods in that it uses deep 3D ConvNet models for feature extraction. 3D ConvNets are capable of extracting spatio-temporal features of the video which are vital for video quality assessment (VQA). Most of the existing FRVQA approaches operate on spatial and temporal domains independently followed by pooling, and often ignore the crucial spatio-temporal relationship of intensities in natural videos. In this work, we pay special attention to the contribution of spatio-temporal dependencies in natural videos to quality assessment. Specifically, the proposed approach estimates the spatio-temporal quality of a video with respect to its pristine version by applying commonly used distance measures such as the l1 or the l2 norm to the volume-wise pristine and distorted 3D ConvNet features. Spatial quality is estimated using off-the-shelf full-reference image quality assessment (FRIQA) methods. Overall video quality is estimated using support vector regression (SVR) applied to the spatio-temporal and spatial quality estimates. Additionally, we illustrate the ability of the proposed approach to localize distortions in space and time.

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IITH Creators:
IITH CreatorsORCiD
Channappayya, SumohanaUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 08 Jul 2019 09:10
Last Modified: 08 Jul 2019 09:10
URI: http://raiith.iith.ac.in/id/eprint/5652
Publisher URL: http://doi.org/10.1109/NCC.2019.8732265
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