# Perceptual Depth Quality Assessment of Stereoscopic Omnidirectional Images

arXiv: https://arxiv.org/abs/2408.10134 · HTML: https://mlsanity.com/paper/2408.10134/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 75 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 16 of 16)

- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency](https://mlsanity.com/paper/2108.06858/) (2021)
- [dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs](https://mlsanity.com/paper/1904.06505/) (2019)
- [Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approach](https://mlsanity.com/paper/1609.04757/) (2016)
- [A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment](https://mlsanity.com/paper/1607.06140/) (2016)
- [Perceptual Quality Assessment of Omnidirectional Images](https://mlsanity.com/paper/2207.02674/) (2022)
- [Blind Omnidirectional Image Quality Assessment with Viewport Oriented Graph Convolutional Networks](https://mlsanity.com/paper/2002.09140/) (2020)
- [No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness](https://mlsanity.com/paper/2102.11393/) (2021)
- [Assessing Visual Quality of Omnidirectional Videos](https://mlsanity.com/paper/1709.06342/) (2017)
- [Study of 3D Virtual Reality Picture Quality](https://mlsanity.com/paper/1910.03074/) (2019)
- [Tensor Oriented No-Reference Light Field Image Quality Assessment](https://mlsanity.com/paper/1909.02358/) (2019)
- [Adaptive Hypergraph Convolutional Network for No-Reference 360-degree Image Quality Assessment](https://mlsanity.com/paper/2105.09143/) (2021)
- [No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement](https://mlsanity.com/paper/1908.06280/) (2019)
- [Stereoscopic Omnidirectional Image Quality Assessment Based on Predictive Coding Theory](https://mlsanity.com/paper/1906.05165/) (2019)
- [Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports](https://mlsanity.com/paper/1908.10079/) (2019)

## Built on by (most cited first, 2 of 2)

- [M2S2L: Mamba-based Multi-Scale Spatial-temporal Learning for Video Anomaly Detection](https://mlsanity.com/paper/2511.05564/) (2025)
- [Blind Stereoscopic Omnidirectional Image Quality Assessment Using Predictive Coding Hierarchy](https://mlsanity.com/paper/2608.28798/) (2026)

## Abstract (arXiv)

Depth perception plays an essential role in the viewer experience for immersive virtual reality (VR) visual environments. However, previous research investigations in the depth quality of 3D/stereoscopic images are rather limited, and in particular, are largely lacking for 3D viewing of 360-degree omnidirectional content. In this work, we make one of the first attempts to develop an objective quality assessment model named depth quality index (DQI) for efficient no-reference (NR) depth quality assessment of stereoscopic omnidirectional images. Motivated by the perceptual characteristics of the human visual system (HVS), the proposed DQI is built upon multi-color-channel, adaptive viewport selection, and interocular discrepancy features. Experimental results demonstrate that the proposed method outperforms state-of-the-art image quality assessment (IQA) and depth quality assessment (DQA) approaches in predicting the perceptual depth quality when tested using both single-viewport and omnidirectional stereoscopic image databases. Furthermore, we demonstrate that combining the proposed depth quality model with existing IQA methods significantly boosts the performance in predicting the overall quality of 3D omnidirectional images.