Progressive Studies Over Sparseness of Least Squares Support Vector Machines based on Hypothetical Assumption
Abstract: No Reference (NR) Video Quality Assessment is the one which is most needed in situations where the handiness of reference video is partially available which is our Hypothetical assumption due to issue raised by reviewers since we used DCT Coefficients in our past research work. Our research work explores the tradeoffs between quality prediction and Video compression. Therefore, we implemented least square support vector regression algorithm as NR-based Video Quality Metric (VQM) for quality estimation with simplified input features based on DCT coefficients (Hypothetical assumption). We concluded that our proposed model overcame sparseness due to hypothetical Assumption.
Keywords: VQM, MOS, LSSVM
How to Cite?: Amitesh Kumar Singam, Venkat Raj Reddy Pashike, "Progressive Studies Over Sparseness of Least Squares Support Vector Machines based on Hypothetical Assumption", Volume 11 Issue 11, November 2022, International Journal of Science and Research (IJSR), Pages: 1059-1060, https://www.ijsr.net/getabstract.php?paperid=SR221118143746, DOI: https://dx.doi.org/10.21275/SR221118143746