Visual quality assessment : recent developments, coding applications and future trends
Tsung-Jung Liu,Yu-Chieh Lin,Weisi Lin,C.-C. Jay Kuo +3 more
- 01 Jan 2013
- Vol. 2
TL;DR: This work provides an in-depth review of recent developments in the field of visual quality assessment and puts equal emphasis on video quality databases and metrics as this is a less investigated area.
read more
Abstract: Research on visual quality assessment has been active during the last decade. In this work, we provide an in-depth review of recent developments in the field. As compared with existing survey papers, our current work has several unique contributions. First, besides image quality databases and metrics, we put equal emphasis on video quality databases and metrics as this is a less investigated area. Second, we discuss the application of visual quality evaluation to perceptual coding as an example for applications. Third, we benchmark the performance of state-of-the-art visual quality metrics with experiments. Finally, future trends in visual quality assessment are discussed.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
DeepFovea: neural reconstruction for foveated rendering and video compression using learned statistics of natural videos
Anton S. Kaplanyan,Anton Sochenov,Thomas Leimkühler,Mikhail I. Okunev,Todd Goodall,Gizem Rufo +5 more
TL;DR: This work explores a novel foveated reconstruction method that employs the recent advances in generative adversarial neural networks to reconstruct a plausible peripheral video from a small fraction of pixels provided every frame.
135
Machine learning in scanning transmission electron microscopy
Sergei V. Kalinin,Colin Ophus,Paul M. Voyles,Rolf Erni,Demie Kepaptsoglou,Vincenzo Grillo,Andrew R. Lupini,Mark P. Oxley,Eric Schwenker,Maria K. Y. Chan,Joanne Etheridge,Xiang Li,Grace G. D. Han,Maxim Ziatdinov,Naoya Shibata,Stephen J. Pennycook +15 more
TL;DR: The integration of machine learning and STEM is focused on to improve user experience and enhance current opportunities in STEM imaging, including the integration of machine learning and STEM to improve user experience and enhance current opportunities in STEM imaging.
98
An Introduction to Neural Data Compression
TL;DR: This introduction hopes to fill in the necessary background by reviewing basic coding topics such as entropy coding and rate-distortion theory, related machine learning ideas such as bits-back coding and perceptual metrics, and providing a guide through the representative works in the literature so far.
96
A ParaBoost Method to Image Quality Assessment
TL;DR: An ensemble method for full-reference image quality assessment (IQA) based on the parallel boosting (ParaBoost) idea is proposed, which outperforms existing IQA methods by a significant margin.
95
No-Reference Image Quality Assessment by Wide-Perceptual-Domain Scorer Ensemble Method
Tsung-Jung Liu,Kuan-Hsien Liu +1 more
TL;DR: A no-reference (NR) learning-based approach to assess image quality is presented, and the proposed NR image quality assessment models are robust with respect to more than 24 image distortion types.
86
References
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Multiscale structural similarity for image quality assessment
Zhou Wang,Eero P. Simoncelli,Alan C. Bovik +2 more
- 09 Nov 2003
TL;DR: This paper proposes a multiscale structural similarity method, which supplies more flexibility than previous single-scale methods in incorporating the variations of viewing conditions, and develops an image synthesis method to calibrate the parameters that define the relative importance of different scales.
FSIM: A Feature Similarity Index for Image Quality Assessment
TL;DR: A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features.
Image information and visual quality
Hamid R. Sheikh,Alan C. Bovik +1 more
TL;DR: An image information measure is proposed that quantifies the information that is present in the reference image and how much of this reference information can be extracted from the distorted image and combined these two quantities form a visual information fidelity measure for image QA.
3.9K
Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures
Zhou Wang,Alan C. Bovik +1 more
TL;DR: This article has reviewed the reasons why people want to love or leave the venerable (but perhaps hoary) MSE and reviewed emerging alternative signal fidelity measures and discussed their potential application to a wide variety of problems.
3.2K