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.

Feature-based prediction of streaming video QoE: Distortions, stalling and memory

Abstract: Mobile streaming video data accounts for a large and increasing percentage of wireless network traffic. The available bandwidths of modern wireless networks are often unstable, leading to difficulties in delivering smooth, high-quality video. Streaming service providers such as Netflix and YouTube attempt to adapt their systems to adjust in response to these bandwidth limitations by changing the video bitrate or, failing that, allowing playback interruptions (stalling). Being able to predict end users’ quality of experience (QoE) resulting from these adjustments could lead to perceptually-driven network resource allocation strategies that would deliver streaming content of higher quality to clients, while being cost effective for providers. To this end, a number of QoE predictors have been developed, but they do not always capture the interplay between video quality and stalling. Towards more effectively predicting user QoE, we have developed a QoE prediction model called Video Assessment of TemporaL Artifacts and Stalls (Video ATLAS), which is a feature-based approach that combines a number of QoE-related features, including perceptually-relevant quality features, stalling-aware features and memory-driven features to make QoE predictions. We evaluated Video ATLAS on the recently designed LIVE-Netflix Video QoE Database which consists of practical playout patterns, where the videos are afflicted by both quality changes and stalling events, and found that it provides improved performance over state-of-the-art video quality metrics while generalizing well on a different dataset. The proposed algorithm is made publicly available at http://live.ece.utexas.edu/research/VideoATLAS/vatlas_index.html .

Pixel-by-pixel classification of MFISH images

Abstract: 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.

Local bandwidth constrained fast inverse motion compensation for DCT-domain video transcoding

Abstract: Discrete cosine transform (DCT) based digital video coding standards, such as MPEG and H.26x, are becoming more widely adopted for multimedia applications. Since the standards differ in their format and syntax, video transcoding, where a compressed video bit-stream is converted from one format to another, is of interest for purposes such as channel bandwidth adaptation and video composition. DCT-domain video transcoding is generally more efficient than spatial-domain transcoding. However, since the data is organized block by block in the DCT domain, inverse motion compensation becomes the bottleneck for DCT-domain methods. We propose a novel local bandwidth constrained fast inverse motion compensation algorithm operating in the DCT domain. Relative to Chang's algorithm, we achieve computational improvement of 25%-55% without visual degradation. A by-product of our algorithm is a reduction of blocking artifacts in very low bit-rate compressed video sequences. The proposed algorithm can be combined with other reported fast methods for more computational savings. We also present a look-up-table (LUT) based implementation method by modeling the statistical distribution of the DCT coefficients in natural images and video sequences. By this method, we obtain a further 31%-48% improvement in computation. The memory requirement of the LUT is about 800 kB, which is reasonable. Moreover, the LUT can be shared by multiple DCT-domain video processing applications running on the same computer or video server.

Minimum entropy segmentation applied to multi-spectral chromosome images

Abstract: In the early 1990s, the state-of-the-art in commercial chromosome image acquisition was grayscale Automated chromosome classification was based on the grayscale image and boundary information obtained during segmentation Multi-spectral image acquisition was developed in 1990 and commercialized in the mid-1990s One acquisition method, multiplex fluorescence in-situ hybridization (M-FISH), uses five color dyes We propose a segmentation algorithm for M-FISH images that minimizes the entropy of classified pixels within possible chromosomes This method is shown to correctly decompose even difficult clusters of touching and overlapping chromosomes Finally, an example image is given to illustrate the algorithm

Optimal Channel Adaptation of Scalable Video Over a Multicarrier-Based Multicell Environment

Abstract: To achieve seamless multimedia streaming services over wireless networks, it is important to overcome inter-cell interference (ICI), particularly in cell border regions. In this regard scalable video coding (SVC) has been actively studied due to its advantage of channel adaptation. We explore an optimal solution for maximizing the expected visual entropy over an orthogonal frequency division multiplexing (OFDM)-based broadband network from the perspective of cross-layer optimization. An optimization problem is parameterized by a set of source and channel parameters that are acquired along the user location over a multicell environment. A suboptimal solution is suggested using a greedy algorithm that allocates the radio resources to the scalable bitstreams as a function of their visual importance. The simulation results show that the greedy algorithm effectively resists ICI in the cell border region, while conventional nonscalable coding suffers severely because of ICI.

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