Classification Review of Raw Subjective Scores Towards Statistical Analysis

EasyChair Preprint 9452

Abstract

In case of Supervised Learning methods, even hypothetically it’s Impossible to combine Speech with Visual characteristics together. Based on ITU Recommendations, we initially assumed and considered the typical two-dimensional plane. In technical terms it should be Chrominance and Luminance plane. In order to quantify impairments of spatial and temporal domain, firstly it should be based on technical assumptions, i.e, we should do mathematical operations based on spatial information within chrominance plane and temporal information within luminance plane. Secondly colour domain exists between two planes and moreover, Scope of subjective quality assessment is essential towards subjective scores as independent variables. But even independent variables are limited to few concepts, out of limited issue, after investigating Human Visual Characteristics, selectively subjective scores are considered as true values judged by humans and We concluded that even after achieving consistency within subjective scores, hypothetically we must assume that our test configuration as sampling distribution not normal because after our investigation we concluded that human visualization characteristics are considered as independent variables

Keyphrases: H.264, ITU, QoE, SSCQ

@booklet{EasyChair:9452,
  author    = {Amitesh Singam},
  title     = {Classification Review of Raw Subjective Scores Towards Statistical Analysis},
  howpublished = {EasyChair Preprint 9452},
  year      = {EasyChair, 2022}}