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No-Reference Video Quality Assessment Using Natural Spatiotemporal Scene Statistics

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Citation:

If you are using the code/model/data provided here in a publication, please cite our paper:

@ARTICLE{9059006,
author={S. V. {Reddy Dendi} and S. S. {Channappayya}},
journal={IEEE Transactions on Image Processing},
title={No-Reference Video Quality Assessment Using Natural Spatiotemporal Scene Statistics},
year={2020},
volume={29},
number={},
pages={5612-5624},}

This is a two-step approach i.e. spatiotemporal feature extraction followed by regression against subjective quality scores. To evaluate the performance of this approach on a given video quality assessment (VQA) dataset, we advise to follow the following steps.

Step1: Feature extraction using functions in FeatureExtraction folder.

   (i) 3D-MSCN features using SpatioTemporal_3DMSCN_Features.m
   (ii) Spatiotemporal Gabor filter based features using SpatioTemporal_I_Features.m and SpatioTemporal_Q_Features.m (both inphase and quadrature)
   

Step2: Performance evaluation using functions in PerformanceEvaluation folder.

   (i) VQA_using_3DMSCN.m evaluates the performance using only 3D-MSCN features.
   (ii) VQA_using_ST_features.m evaluates the performance using only spatiotemporal Gabor filter-based features.
   (ii) VQA_using_3DMSCN_and_ST_features.m evaluates the perfomance using 3D-MSCN and spatiotemporal Gabor filter based features.

Details about other folders.

   (i) videos: You can place videos here to extracts its features.
   (ii) src: Contains 3D MSCN function, AGGD parameter estimate, and Gabor 3D Kernal.
   (iii) matlab_yuvread: Contains functions to read .yuv videos.
   (iv) logistic fitting: Contain logistic regression function to find LCC and SRCC between objective and subjective scores.