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 Coefcients 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 simplied input features based on DCT coefcients (Hypothetical assumption). We concluded that our proposed model overcame sparseness due to hypothetical Assumption.