Paper Title
Classification Algorithm Analysis for Texture Detection in Block-Based Hybrid Video Coding
Abstract
This study presents a comprehensive comparison of various supervised classification algorithms for texture
detection in the context of block-based hybrid video coding. To accomplish this, a dataset of images extracted directly from
video encoder block partitions was created and manually classified according to their texture levels. The study utilizes the
Mean Directional Variance (MDV) algorithm to extract orientation information from each block in the form of average
variances for specific rational slopes. This vector of variances is then processed to obtain a set of descriptive statistics that
serve as input elements for training and evaluating four popular supervised learning models: Decision Tree (DT), Random
Forest (RF), Support Vector Machine (SVM), and Supervised Neural Networks (SNN). The objective is to identify the most
effective algorithm for accurately classifying texture levels and utilizing this information in perceptual video coding.
Keywords - Classification Algorithms, Texture Detection, MDV, Perceptual Coding.