No-Reference Light Field Image Quality Assessment Based on Micro-Lens Image

TL;DR

This paper introduces a no-reference light field image quality assessment method that leverages micro-lens images to effectively measure 2-D angular consistency and spatial quality, achieving state-of-the-art results.

Contribution

It proposes a novel no-reference LF-IQA model based on micro-lens images, utilizing entropy and pattern descriptors to assess angular and spatial quality.

Findings

Achieves state-of-the-art performance in LF-IQA

Effectively measures 2-D angular consistency

Accurately assesses spatial quality

Abstract

Light field image quality assessment (LF-IQA) plays a significant role due to its guidance to Light Field (LF) contents acquisition, processing and application. The LF can be represented as 4-D signal, and its quality depends on both angular consistency and spatial quality. However, few existing LF-IQA methods concentrate on effects caused by angular inconsistency. Especially, no-reference methods lack effective utilization of 2-D angular information. In this paper, we focus on measuring the 2-D angular consistency for LF-IQA. The Micro-Lens Image (MLI) refers to the angular domain of the LF image, which can simultaneously record the angular information in both horizontal and vertical directions. Since the MLI contains 2-D angular information, we propose a No-Reference Light Field image Quality assessment model based on MLI (LF-QMLI). Specifically, we first utilize Global Entropy Distribution (GED) and Uniform Local Binary Pattern descriptor (ULBP) to extract features from the MLI, and then pool them together to measure angular consistency. In addition, the information entropy of Sub-Aperture Image (SAI) is adopted to measure spatial quality. Extensive experimental results show that LF-QMLI achieves the state-of-the-art performance.