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

Analysis of the statistics of natural scene features at observers’ fixations can help us understand the mechanism of fixation selection and visual attention of the human vision system Previous studies revealed that several low-level luminance features at fixations are statistically different from those at randomly selected locations In our study, we conducted eye tracking experiments on naturalistic stereo images presented through a haploscope and found that fixated luminance contrast and luminance gradient are generally higher than randomly selected luminance contrast and luminance gradient, which agrees with previous literature, but the fixated disparity contrast and disparity gradient are generally lower than randomly selected disparity contrast and disparity gradient We discuss the relevance of our findings in the context of the complexity of disparity calculations and the metabolic needs of disparity processing

/pdf/dichotomy-between-luminance-and-disparity-features-at-22sh5bzli1.pdf

Dichotomy between luminance and disparity features at binocular fixations.

Multiplex Fluorescence In-Situ Hybridization (M-FISH) is a recently developed chromosome imaging method in which each chromosome is labelled with 5 fluors (dyes) and is also counterstained with DAPI. This paper proposes an automatic pixel by pixel classification algorithm for M-FISH images using a Bayes classifier. The M-FISH pixel classification was approached as a 25 class 6 feature pattern recognition problem. The classifier was trained and tested on non-overlapping data sets and an overall classification accuracy of 95% was obtained.

Pixel-by-pixel classification of MFISH images

We propose a new quality metric for range images that is based on the multi-scale Structural Similarity (MS-SSIM) Index. The new metric operates in a manner to SSIM but allows for special handling of missing data. We demonstrate its utility by reevaluating the set of stereo algorithms evaluated in the Middlebury Stereo Vision Page http://vision.middlebury.edu/stereo/. The new algorithm which we term Range SSIM (R-SSIM) Index possesses features that make it an attractive choice for assessing the quality of range images.

Range Image Quality Assessment by Structural Similarity.

Perceptual video quality assessment models are either frame-based or video-based, i.e., they apply spatiotemporal filtering or motion estimation to capture temporal video distortions. Despite their good performance on video quality databases, video-based approaches are time-consuming and harder to efficiently deploy. To balance between high performance and computational efficiency, Netflix developed the Video Multi-method Assessment Fusion (VMAF) framework, which integrates multiple quality-aware features to predict video quality. Nevertheless, this fusion framework does not fully exploit temporal video quality measurements which are relevant to temporal video distortions. To this end, we propose two improvements to the VMAF framework: SpatioTemporal VMAF and Ensemble VMAF. Both algorithms exploit efficient temporal video features which are fed into a single or multiple regression models. To train our models, we designed a large subjective database and evaluated the proposed models against state-of-the-art approaches. The compared algorithms will be made available as part of the open source package in this https URL.

SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment

In this paper, we present an algorithm for designing a linear equalizer that is optimal with respect to the structural similarity (SSIM) index. The optimization problem is shown to be non-convex, thereby making it non-trivial. The non-convex problem is first converted to a quasi-convex problem and then solved using a combination of first order necessary conditions and bisection search. To demonstrate the usefulness of this solution, it is applied to image denoising and image restoration examples. We show using these examples that optimizing equalizers for the SSIM index does indeed result in higher perceptual image quality compared to equalizers optimized for the ubiquitous mean squared error (MSE).

/pdf/ssim-optimal-linear-image-restoration-1dppo9m79t.pdf

Alan C. Bovik

Papers

Dichotomy between luminance and disparity features at binocular fixations.

Pixel-by-pixel classification of MFISH images

Range Image Quality Assessment by Structural Similarity.

SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment

SSIM-optimal linear image restoration