Alan C. Bovik

Bio: Alan C. Bovik is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Image quality & Video quality. The author has an hindex of 102, co-authored 837 publications receiving 96088 citations. Previous affiliations of Alan C. Bovik include University of Illinois at Urbana–Champaign & University of Sydney.

Visual memory for fixated regions of natural images dissociates attraction and recognition

Abstract: Recognition memory for fixated regions from briefly viewed full-screen natural images is examined. Low-level image statistics reveal that observers fixated, on average (pooled across images and observers), image regions that possessed greater visual saliency than non-fixated regions, a finding that is robust across multiple fixation indices. Recognition-memory performance indicates that, of the fixation loci tested, observers were adept at recognising those with a particular profile of image statistics; visual saliency was found to be attenuated for unrecognised loci, despite that all regions were freely fixated. Furthermore, although elevated luminance was the local image statistic found to discriminate least between human and random image locations, it was the greatest predictor of recognition-memory performance, demonstrating a dissociation between image features that draw fixations and those that support visual memory. An analysis of corresponding eye movements indicates that image regions fixated via short-distance saccades enjoyed better recognition-memory performance, alluding to a focal rather than ambient mode of processing. Recognised image regions were more likely to have originated from areas evaluated (a posteriori) to have higher fixation density, a numerical metric of local interest. Surprisingly, memory for image regions fixated later in the viewing period exhibited no recency advantage, despite (typically) also being longer in duration, a finding for which a number of explanations are posited.

Blind quality assessment of JPEG2000 compressed images using natural scene statistics

Abstract: Measurement of image quality is crucial for many image-processing algorithms, such as acquisition, compression, restoration, enhancement and reproduction. Traditionally, researchers in image quality assessment have focused on equating image quality with similarity to a 'reference' or 'perfect' image. The field of blind, or no-reference, quality assessment, in which image quality is predicted without the reference image, has been largely unexplored. In this paper, we present a blind quality assessment algorithm for images compressed by JPEG2000 using natural scene statistics (NSS) modelling. We show how reasonably comprehensive NSS models can help us in making blind, but accurate, predictions of quality. Our algorithm performs close to the limit imposed on useful prediction by the variability between human subjects.

Full-reference quality assessment of stereoscopic images by modeling binocular rivalry

Abstract: We develop a framework for assessing the quality of stereoscopic images that have been afflicted by possibly asymmetric distortions An intermediate image is generated which when viewed stereoscopically is designed to have a perceived quality close to that of the cyclopean image We hypothesize that performing stereoscopic QA on the intermediate image yields higher correlations with human subjective judgments The experimental results confirm the hypothesis and show that the proposed framework significantly outperforms conventional 2D QA metrics when predicting the quality of stereoscopically viewed images that may have been asymmetrically distorted

Spectral properties of moving L-estimates of independent data

Abstract: A derivation of the joint probability distribution and mass functions of order statistics coming from overlapping samples is presented. The general formulation allows for samples of any size overlapping (coinciding) in any number of observed values ranging from zero to the number of observations in the smaller sample. These expressions are used to compute the autocovariance function of a moving L -estimate (linear combination of order statistics) of a sequence of independent, identically distributed second-order random variables, under a variety of assumptions on the parent distribution. The associated variance spectral density is also computed for several filters of interest, including median filters, and inner and outer trimmed mean filters.

Image quality assessment: from error visibility to structural similarity

Abstract: 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/.

A wavelet tour of signal processing

Abstract: 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.

Image-to-Image Translation with Conditional Adversarial Networks

Abstract: 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.

Image-to-Image Translation with Conditional Adversarial Networks

Abstract: 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.

Phd by thesis

Abstract: 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