A ParaBoost Stereoscopic Image Quality Assessment (PBSIQA) System

TL;DR

This paper introduces a novel ParaBoost stereoscopic image quality assessment system that classifies distortions, develops specialized quality scorers, and fuses their scores for improved 3D image quality evaluation.

Contribution

It proposes a new two-stage PBSIQA system combining distortion classification, specialized scorers, and parallel boosting fusion, enhancing stereoscopic image quality assessment.

Findings

Outperforms existing SIQA metrics in experiments

Effective classification of distortion types improves assessment accuracy

Fused scores provide a robust overall quality measure

Abstract

The problem of stereoscopic image quality assessment, which finds applications in 3D visual content delivery such as 3DTV, is investigated in this work. Specifically, we propose a new ParaBoost (parallel-boosting) stereoscopic image quality assessment (PBSIQA) system. The system consists of two stages. In the first stage, various distortions are classified into a few types, and individual quality scorers targeting at a specific distortion type are developed. These scorers offer complementary performance in face of a database consisting of heterogeneous distortion types. In the second stage, scores from multiple quality scorers are fused to achieve the best overall performance, where the fuser is designed based on the parallel boosting idea borrowed from machine learning. Extensive experimental results are conducted to compare the performance of the proposed PBSIQA system with those of existing stereo image quality assessment (SIQA) metrics. The developed quality metric can serve as an objective function to optimize the performance of a 3D content delivery system.