Modeling sparse spatio-temporal representations for no-reference video quality assessment

Shabeer, P Muhammed and Bhati, Saurabhchand and Channappayya, Sumohana (2017) Modeling sparse spatio-temporal representations for no-reference video quality assessment. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP), 14-16 November 2017, Montreal, QC, Canada.

Full text not available from this repository. (Request a copy)


We present a novel No-Reference (NR) video quality assessment (VQA) algorithm that operates on the sparse representation 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 dictionaries 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.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Channappayya, SumohanaUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Sparse representation, spatio-temporal volumes, No-Reference video quality assessment
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 14 May 2019 05:08
Last Modified: 14 May 2019 05:08
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 5149 Statistics for this ePrint Item