Tensor Oriented No-Reference Light Field Image Quality Assessment

TL;DR

This paper introduces a tensor-based no-reference quality assessment method for light field images, capturing both spatial and angular distortions to improve evaluation accuracy in immersive media applications.

Contribution

It proposes a novel tensor-oriented approach using Tucker decomposition and new quality indices, advancing LFI quality assessment beyond existing 2D, 3D, and multi-view methods.

Findings

Outperforms state-of-the-art LFI quality metrics

Effective in capturing spatial and angular distortions

Validated on four public LFI databases

Abstract

Light field image (LFI) quality assessment is becoming more and more important, which helps to better guide the acquisition, processing and application of immersive media. However, due to the inherent high dimensional characteristics of LFI, the LFI quality assessment turns into a multi-dimensional problem that requires consideration of the quality degradation in both spatial and angular dimensions. Therefore, we propose a novel Tensor oriented No-reference Light Field image Quality evaluator (Tensor-NLFQ) based on tensor theory. Specifically, since the LFI is regarded as a low-rank 4D tensor, the principal components of four oriented sub-aperture view stacks are obtained via Tucker decomposition. Then, the Principal Component Spatial Characteristic (PCSC) is designed to measure the spatial-dimensional quality of LFI considering its global naturalness and local frequency properties. Finally, the Tensor Angular Variation Index (TAVI) is proposed to measure angular consistency quality by analyzing the structural similarity distribution between the first principal component and each view in the view stack. Extensive experimental results on four publicly available LFI quality databases demonstrate that the proposed Tensor-NLFQ model outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms.