Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

/pdf/image-quality-assessment-from-error-visibility-to-structural-1rlwcqe34t.pdf

Image quality assessment: from error visibility to structural similarity

Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour. Estimations are Approximations. Transform Coding. Appendix A: Mathematical Complements. Appendix B: Software Toolboxes.

http://ahvazdownload.persiangig.com/document/article/39.pdf

A wavelet tour of signal processing

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either.

/pdf/image-to-image-translation-with-conditional-adversarial-5058t24et2.pdf

Image-to-Image Translation with Conditional Adversarial Networks

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Many existing no-reference image quality approaches exploit only univariate statistical models of bandpass image coefficients, thereby neglecting the higher-order correlations that occur between adjacent coefficients which may be modified by distortion We modeled multivariate natural image statistics in the spatial domain as following a Multivariate Generalized Gaussian distribution that provides useful information regarding the type and severity of distortions in image signals We have also found that gaussianity assumptions when estimating local variances are violated in the presence of distortions, hence we estimate local energies using a generalized Gaussian distribution To this end, we propose Multivariate-Generalized Contrast Normalization (MV-GCN): a multivariate approach which integrates a generalized contrast normalization step We demonstrate the potential of our model in various image quality applications

Multivariate Statistics for Blind Image Quality Applications

Professor H.A. David has kindly pointed out that an expression for the joint probability distribution function of order statistics from overlapping samples, similar to equation (3) in our paper, had been previously given in the reference cited below. This expression could also have been used in deriving the results obtained later in our paper.

Acknowledgement of priority spectral properties of moving L-estimates of independent data

Publisher Summary This chapter explains the popular courseware for image processing education known as SIVA—the signal, image, and video audiovisualization—gallery. The image and video processing section of the SIVA gallery consists of a suite of special purpose LabVIEW-based programs known as Virtual Instruments (VIs). Equipped with informative visualization and a user-friendly interface, these VIs were carefully designed to facilitate a gentle introduction to the fascinating concepts in image and video processing. To make things easier, the demos are accompanied by a comprehensive set of help files that describe the various controls, and that highlight some illustrative examples and instructive parameter settings. A demo can be activated by clicking the rightward pointing arrow in the top menu bar. Help for the demo can be activated by clicking the ‘?’ button and moving the cursor over the icon that is located immediately to the right of the ‘?’ button. In addition, when the cursor is placed over any other button/control, the help window automatically updates to describe the function of that button/control. The user will find this visual, hands-on, interactive introduction to image processing to be a fun, enjoyable, and illuminating experience.

The SIVA Image Processing Demos

Perceptual Video Quality Assessment (VQA) is one of the most fundamental and challenging problems in the field of Video Engineering. Along with video compression, it has become one of two dominant theoretical and algorithmic technologies in television streaming and social media. Over the last 2 decades, the volume of video traffic over the internet has grown exponentially, powered by rapid advancements in cloud services, faster video compression technologies, and increased access to high-speed, low-latency wireless internet connectivity. This has given rise to issues related to delivering extraordinary volumes of picture and video data to an increasingly sophisticated and demanding global audience. Consequently, developing algorithms to measure the quality of pictures and videos as perceived by humans has become increasingly critical since these algorithms can be used to perceptually optimize trade-offs between quality and bandwidth consumption. VQA models have evolved from algorithms developed for generic 2D videos to specialized algorithms explicitly designed for on-demand video streaming, user-generated content (UGC), virtual and augmented reality (VR and AR), cloud gaming, high dynamic range (HDR), and high frame rate (HFR) scenarios. Along the way, we also describe the advancement in algorithm design, beginning with traditional hand-crafted feature-based methods and finishing with current deep-learning models powering accurate VQA algorithms. We also discuss the evolution of Subjective Video Quality databases containing videos and human-annotated quality scores, which are the necessary tools to create, test, compare, and benchmark VQA algorithms. To finish, we discuss emerging trends in VQA algorithm design and general perspectives on the evolution of Video Quality Assessment in the foreseeable future.

/pdf/perceptual-video-quality-assessment-the-journey-continues-1fe11dh7.pdf

Perceptual video quality assessment: the journey continues!

In this talk I will discuss our recent efforts on blind or “no reference” image quality assessment problems, including machine learning approaches and the looming question of stereo (3-D) image quality.

Alan C. Bovik

Papers

Multivariate Statistics for Blind Image Quality Applications

Acknowledgement of priority spectral properties of moving L-estimates of independent data

The SIVA Image Processing Demos

Perceptual video quality assessment: the journey continues!

Blind Image Quality Assessment is Not Impossible Plenary Talk