☆ 4.6 Article

No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System

APPLIED SCIENCES-BASEL (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Engineering, Electrical & Electronic

Deep Learning-Based Perceptual Video Quality Enhancement for 3D Synthesized View

Huan Zhang et al.

Summary: This paper proposes a deep Convolutional Neural Network (CNN)-based algorithm to denoise synthesized videos and reduce temporal flicker distortion. The proposed method analyzes spatio-temporal distortion and models it as a perceptual video denoising problem. Experimental results show that the proposed method outperforms deep learning-based counterparts and conventional denoising methods, significantly enhancing the perceptual quality of 3D synthesized videos.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2022)

添加到收藏夹

Article Chemistry, Analytical

No-Reference Video Quality Assessment Using Multi-Pooled, Saliency Weighted Deep Features and Decision Fusion

Domonkos Varga

Summary: This paper presents an innovative deep learning-based approach for no-reference video quality assessment (NR-VQA) that utilizes parallel pre-trained convolutional neural networks to extract deep features and map them to perceptual quality scores. The quality scores from different regressors are then fused to obtain the overall perceptual quality of a video sequence. Experimental results show that the proposed method achieves state-of-the-art performance on benchmark databases with authentic distortions, and the fusion of multiple deep architectures significantly improves NR-VQA.

SENSORS (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

No-reference stereoscopic image quality assessment based on global and local content characteristics

Lili Shen et al.

Summary: This paper proposes a no-reference stereoscopic image quality assessment method based on global and local content characteristics. By simulating the perception route of the human visual system, the method efficiently extracts features from fused view and single view, achieving excellent performance in quality assessment.

NEUROCOMPUTING (2021)

添加到收藏夹

Article Computer Science, Information Systems

No-reference stereoscopic image quality evaluator based on human visual characteristics and relative gradient orientation

Yun Liu et al.

Summary: A new no-reference SIQA model is built in this work, which combines monocular and binocular features to predict binocular quality perception by extracting statistical quality-aware structural features and texture features. The proposed model achieves high consistency with subjective assessment and competitive performance with the latest models on popular stereoscopic image databases.

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION (2021)

添加到收藏夹

Article Computer Science, Information Systems

No-Reference Video Quality Assessment Based on Benford's Law and Perceptual Features

Domonkos Varga

Summary: This paper discusses the significance and research status of No-reference video quality assessment (NR-VQA), proposes a powerful feature vector inspired by Benford's law, and demonstrates its effectiveness through experiments on two large, authentically distorted VQA benchmark databases.

ELECTRONICS (2021)

添加到收藏夹

Article Physics, Multidisciplinary

No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis

Chongchong Jin et al.

Summary: This study proposes a no-reference 3D synthesized image quality assessment method based on visual-entropy-guided multi-layer feature analysis, dividing geometric distortions into bottom-up and top-down layers of visual attention, and integrating features from both layers to establish a more visually perceptive quality evaluation model.

ENTROPY (2021)

添加到收藏夹

Proceedings Paper Acoustics

MULTI-SCALE FEATURE-GUIDED STEREOSCOPIC VIDEO QUALITY ASSESSMENT BASED ON 3D CONVOLUTIONAL NEURAL NETWORK

Yingjie Feng et al.

Summary: This paper proposed a novel multi-scale feature-guided 3D convolutional neural network for stereoscopic video quality assessment. By introducing a new multi-scale unit and a multi-stage growing attention mechanism, the proposed method outperformed existing methods significantly in correlating with human visual perception.

2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

No-Reference Video Quality Assessment Using Natural Spatiotemporal Scene Statistics

Sathya Veera Reddy Dendi et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Blind Prediction of Natural Video Quality

Michele A. Saad et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2014)

添加到收藏夹

Article Engineering, Electrical & Electronic

Quality of Experience for 3D Video Streaming

Chaminda T. E. R. Hewage et al.

IEEE COMMUNICATIONS MAGAZINE (2013)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Toward an Impairment Metric for Stereoscopic Video: A Full-Reference Video Quality Metric to Assess Compressed Stereoscopic Video

Varuna De Silva et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2013)

添加到收藏夹

Article Computer Science, Theory & Methods

A perceptual metric for stereoscopic image quality assessment based on the binocular energy

Rafik Bensalma et al.

MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING (2013)

添加到收藏夹

Article Engineering, Electrical & Electronic

An Objective Video Quality Metric for Compressed Stereoscopic Video

Jungdong Seo et al.

CIRCUITS SYSTEMS AND SIGNAL PROCESSING (2012)

添加到收藏夹

Article Engineering, Electrical & Electronic

Perceived 3D TV Transmission Quality Assessment: Multi-Laboratory Results Using Absolute Category Rating on Quality of Experience Scale

Kun Wang et al.

IEEE TRANSACTIONS ON BROADCASTING (2012)

添加到收藏夹

Article Engineering, Electrical & Electronic

Stereoscopic Perceptual Video Coding Based on Just-Noticeable-Distortion Profile

Lei Zhang et al.

IEEE TRANSACTIONS ON BROADCASTING (2011)

添加到收藏夹

Article Neurosciences

Binocular Rivalry Requires Visual Attention

Peng Zhang et al.

NEURON (2011)

添加到收藏夹

Article Engineering, Electrical & Electronic

Improved Resilience for Video Over Packet Loss Networks with MDC and Optimized Packetization

Moyuresh Biswas et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2009)

添加到收藏夹

Article Imaging Science & Photographic Technology

Visual Discomfort and Visual Fatigue of Stereoscopic Displays: A Review

Marc Lambooij et al.

JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY (2009)

添加到收藏夹

Article Engineering, Electrical & Electronic

Quality Analysis for 3D Video Using 2D Video Quality Models

S. L. P. Yasakethu et al.

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS (2008)

添加到收藏夹

Article Engineering, Electrical & Electronic

Quality Assessment of Stereoscopic Images

Alexandre Benoit et al.

EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING (2008)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Image quality assessment: From error visibility to structural similarity

Z Wang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2004)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.