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.

Asymptotic convergence of biorthogonal wavelet filters

Abstract: We study the asymptotic behavior of the dual filters associated with biorthogonal spline wavelets (BSWs) and general biorthogonal Coifman wavelets (GBCWs). As the order of wavelet systems approaches infinity the BSW filters either diverge or converge to some non-ideal filters, the GBCW synthesis filters converge to an ideal halfband lowpass (HBLP) filter without exhibiting any Gibbs-like phenomenon, and a subclass of the analysis filters also converge to an ideal HBLP filter but with a one-sided Gibbs-like behavior. The two approximations of the ideal HBLP filter by Daubechies orthonormal wavelet filters and by the GBCW synthesis filters are also compared.

Capturing Video Frame Rate Variations through Entropic Differencing.

Abstract: High frame rate videos are increasingly getting popular in recent years majorly driven by strong requirements by the entertainment and streaming industries to provide high quality of experiences to consumers. To achieve the best trade-off between the bandwidth requirements and video quality in terms of frame rate adaptation, it is imperative to understand the effects of frame rate on video quality. In this direction, we make two contributions: firstly we design a High Frame Rate (HFR) video database consisting of 480 videos and around 19,000 human quality ratings. We then devise a novel statistical entropic differencing method based on Generalized Gaussian Distribution model in spatial and temporal band-pass domain, which measures the difference in quality between the reference and distorted videos. The proposed design is highly generalizable and can be employed when the reference and distorted sequences have different frame rates, without any need of temporal upsampling. We show through extensive experiments that our model correlates very well with subjective scores in the HFR database and achieves state of the art performance when compared with existing methodologies.

ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression

Abstract: The use of $\ell_p$ $(p=1,2)$ norms has largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties. However, when used to assess the loss of visual information, these simple norms are not very consistent with human perception. Here, we describe a different "proximal" approach to optimize image analysis networks against quantitative perceptual models. Specifically, we construct a proxy network, broadly termed ProxIQA, which mimics the perceptual model while serving as a loss layer of the network. We experimentally demonstrate how this optimization framework can be applied to train an end-to-end optimized image compression network. By building on top of an existing deep image compression model, we are able to demonstrate a bitrate reduction of as much as $31\%$ over MSE optimization, given a specified perceptual quality (VMAF) level.

Shape change analysis and shape modeling using three dimensional biomedical images

Abstract: A method for computing material deformations from multidimensional images of a biological specimen undergoing shape changes is presented. The specimen data are segmented within the images and the domain of the specimen is parametrized. The parametrization is a material coordinate system for the specimen. Deformations of the material coordinate system are computed by minimizing an energy functional that is a linear combination of a brightness continuity constraint and a shape-change constraint based on differential geometric properties of the parametrization. The resulting parametrization completely describes the position and shape-change of the specimen for the entire image sequence. This approach was applied to images of a motile human white blood cell. >

DEMODULATION OF IMAGES MOD MODULATIONS USING MULTIDIlL ELED BY AMPLITUDE-FREQUENCY :ENSIONAL ENERGY SEPARATION

Abstract: Locally narrowband images can be modeled as 2D spatial AM-FM signals with several applications in image texture analysis and computer vision. In this paper we formulate such an image demodulation problem, and present a solution based on the multidimensional energy operator @(f) = llVf112 - fV2 f. We discuss some interesting properties of this multidimensional operator and develop multidimensional energy separation algorithms to estimate the amplitude envelope and instantaneous frequencies of 2D spatially-varying AM-FM signals. Experiments are also presented on applying this 2D energy demodulation algorithm to estimate the instantaneous amplitude contrast and spatial frequencies of image textures bandpass filtered via Gabor filters. The attractive features of the multidimensional energy operator and the 2D energy separation algorithm are their simplicity, efficiency, and ability to track instantaneously-varying spatial modulation patterns.

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