Perceptual visual quality metrics: A survey

Wang, Lin; Kuo, C.-C. Jay

doi:10.1016/j.jvcir.2011.01.005

Cited by 839 publications

(373 citation statements)

References 154 publications

(221 reference statements)

Supporting

Mentioning

370

Contrasting

Unclassified

Order By: Relevance

“…), and that for the kind of artifact which is introduced, the squared error measure serves its purpose apparently quite well. Still, there has been considerable work on the topic of perceptual visual quality metrics, see [LK11]. In particular, perceptual distortion measures based on the total variation (TV) [ROF92] have been proposed recently [PSGF11,WZD14].…”

Section: Discussionmentioning

confidence: 99%

Multi-frame optimized quantization for high efficiency video coding

Winken

Roth

Schwarz

et al. 2015

2015 Picture Coding Symposium (PCS)

View full text Add to dashboard Cite

In video coding, there are inter-frame dependencies due to motion-compensated prediction. The achievable rate distortion performance of an inter-coded frame depends on the coding decisions made during the encoding of its reference frames. Typically, in the encoding of a reference frame, these dependencies are either not considered at all or only via some rough heuristic.In this thesis, a multi-frame transform coefficient optimization method for H.265/ HEVC is developed and studied. The inter-frame dependencies are described using a linear signal model. Based on this model, the optimization problem is cast in the form of an 1 -regularized least squares problem. For solving this problem, an optimization algorithm is developed, which is applicable to H.265/HEVC without imposing excessive demands in terms of computational complexity and memory requirements. A simple functional relationship between the regularization parameter and the quantization paramter is empirically found. The accuracy of the linear signal model is studied, the bit rate savings due to the proposed method are evaluated, and its complexity is assessed. Finally, an extension of the method for spatially scalable video coding using SVC, the scalable extension of H.264/AVC, is presented.iii ZusammenfassungBei der Codierung von Videosignalen ergeben sich aufgrund der bewegungskompensierten Prädiktion Abhängigkeiten zwischen den einzelnen Frames. Die erzielbare Rate-Distortion-Performance eines inter-codierten Frames hängt dadurch von den Codierentscheidungen ab, die bei der Codierung seiner Referenzbilder getroffen wurden. Typischerweise werden diese Abhängigkeiten bei der Codierung eines Referenzbildes entweder gar nicht beachtet oder aber nur mittels einer groben Heuristik.

show abstract

Section: Discussionmentioning

confidence: 99%

Multi-frame optimized quantization for high efficiency video coding

Winken

Roth

Schwarz

et al. 2015

2015 Picture Coding Symposium (PCS)

View full text Add to dashboard Cite

show abstract

“…With the widespread use of digital images, images are usually affected by a wide variety of distortions during acquisition, compression, storage, transmission and reproduction, resulting in perceptual quality degradation [1]- [5]. Quantification and assessment for such degradation of image quality play an important role in image processing system.…”

Section: Introductionmentioning

confidence: 99%

“…Such feature-based methods are inclined to effectively extract quality-aware features and have achieved notable success [4], [5], [13]. Among these feature-based methods, structural similarity index (SSIM) [10] is quite attractive and popular owing to its simplicity and excellent performance, which is based on the hypothesis that HVS is highly adapted for extracting structural information from images.…”

Section: Introductionmentioning

confidence: 99%

Image Quality Assessment Based on Multi-Order Local Features Description, Modeling and Quantification

Ding

Zhao

Zhi

et al. 2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYImage quality assessment (IQA) plays an important role in quality monitoring, evaluation and optimization for image processing systems. However, current quality-aware feature extraction methods for IQA can hardly balance accuracy and complexity. This paper introduces multi-order local description into image quality assessment for feature extraction. The first-order structure derivative and high-order discriminative information are integrated into local pattern representation to serve as the quality-aware features. Then joint distributions of the local pattern representation are modeled by spatially enhanced histogram. Finally, the image quality degradation is estimated by quantifying the divergence between such distributions of the reference image and those of the distorted image. Experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches in consideration of not only accuracy that is consistent with human subjective evaluation, but also robustness and stability across different distortion types and various public databases. It provides a promising choice for image quality assessment development. key words: image quality assessment, image quality degradation, local pattern representation, feature extraction, visual perception

show abstract

“…This visual fidelitybased approach is more attractive because of its practicality and mathematical foundation [6], [7]. The majority of these fidelity-based methods attempt to quantify the perceptual quality either in terms of statistical information [8], [9] or in terms of structural information of the images [5], [10]- [14].…”

Section: Introductionmentioning

confidence: 99%

Sparse representation-based image quality assessment

Guha

Nezhadarya

Ward

2014

Signal Processing: Image Communication

View full text Add to dashboard Cite

Abstract-A successful approach to image quality assessment involves comparing the structural information between a distorted and its reference image. However, extracting structural information that is perceptually important to our visual system is a challenging task. This paper addresses this issue by employing a sparse representation-based approach and proposes a new metric called the sparse representation-based quality (SPARQ) index. The proposed method learns the inherent structures of the reference image as a set of basis vectors, such that any structure in the image can be represented by a linear combination of only a few of those basis vectors. This sparse strategy is employed because it is known to generate basis vectors that are qualitatively similar to the receptive field of the simple cells present in the mammalian primary visual cortex [1]. The visual quality of the distorted image is estimated by comparing the structures of the reference and the distorted images in terms of the learnt basis vectors resembling cortical cells. Our approach is evaluated on six publicly available subject-rated image quality assessment datasets. The proposed SPARQ index consistently exhibits high correlation with the subjective ratings on all datasets and performs better or at par with the state-ofthe-art.

show abstract

Perceptual visual quality metrics: A survey

Cited by 839 publications

References 154 publications

Multi-frame optimized quantization for high efficiency video coding

Multi-frame optimized quantization for high efficiency video coding

Image Quality Assessment Based on Multi-Order Local Features Description, Modeling and Quantification

Sparse representation-based image quality assessment

Contact Info

Product

Resources

About