No-reference Perceptual Quality Assessment for Streaming Video Based on Simple End-to-end Network Measures

2006 ◽  
Author(s):  
Shengke Qiu ◽  
Huaxia Rui ◽  
Le Zhang
Keyword(s):  
Quality Assessment ◽  
Streaming Video ◽  
Perceptual Quality ◽  
Network Measures ◽  
End To End

An End-to-End Perceptual Quality Assessment Method via Score Distribution Prediction

2019 ◽  
Vol 51 (3) ◽  
pp. 2123-2137
Author(s):  
Jing Liu ◽  
Jingting Wang ◽  
Weizhi Nie ◽  
Yuting Su ◽  
Anan Liu
Keyword(s):  
Quality Assessment ◽  
Assessment Method ◽  
Perceptual Quality ◽  
Score Distribution ◽  
Quality Assessment Method ◽  
End To End

Perceptual Quality Assessment of Compressed Vibrotactile Signals through Comparative Judgment

2021 ◽  
pp. 1-1
Author(s):  
Evelyn Muschter ◽  
Andreas Noll ◽  
Jinting Zhao ◽  
Rania Hassen ◽  
Matti Strese ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Comparative Judgment ◽  
Perceptual Quality

scholarly journals No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features

2021 ◽  
Vol 7 (7) ◽  
pp. 112
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Multiple Scales ◽  
Digital Images ◽  
Input Image ◽  
Perceptual Quality ◽  
Reference Image ◽  
Benchmark Datasets ◽  
In The Wild

The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ).

Quality Assessment of End-to-End Learned Image Compression

2021 ◽  
Author(s):  
Yang Li ◽  
Shiqi Wang ◽  
Xinfeng Zhang ◽  
Shanshe Wang ◽  
Siwei Ma ◽  
...  
Keyword(s):  
Image Compression ◽  
Quality Assessment ◽  
End To End

Perceptual Quality Assessment of Low-light Image Enhancement

2021 ◽  
Vol 17 (4) ◽  
pp. 1-24
Author(s):  
Guangtao Zhai ◽  
Wei Sun ◽  
Xiongkuo Min ◽  
Jiantao Zhou
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Perceptual Quality ◽  
Low Light ◽  
Light Image ◽  
Light Enhancement

Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.

Perceptual Quality Assessment of Cartoon Images

2021 ◽  
pp. 1-1
Author(s):  
Hangwei Chen ◽  
Xiongli Chai ◽  
Feng Shao ◽  
Xuejin Wang ◽  
Qiuping Jiang ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Perceptual Quality
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