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.

Quality Aware Features for Performance Prediction and Time Reduction in Video Object Tracking

Abstract: The existing body of work on video object tracking (VOT) algorithms has studied various image conditions such as occlusion, clutter, and object shape, which influence video quality and affect tracking performance. Nonetheless, there is no clear distinction between the performance reduction caused by scene-dependent challenges such as occlusion and clutter, and the effect of authentic in-capture and post-capture distortions. Despite the plethora of VOT methods in the literature, there is a lack of detailed studies analyzing the performance of videos with authentic in-capture and post-capture distortions. We introduced a new dataset of authentically distorted videos (AD-SVD) to address this issue. This dataset contains 4476 videos with different authentic distortions and surveillance activities. Furthermore, it provides benchmarking results for evaluating ten state-of-the-art visual object trackers (from VOT 2017-2018 challenges) based on the proposed dataset. In addition, this study develops an approach for performance prediction and quality-aware feature selection for single-object tracking in authentically distorted surveillance videos. The method predicts the performance of a VOT algorithm with high accuracy. Then, the probability of obtaining the reference output is maximized without executing the tracking algorithms. We also propose a framework to reduce video tracker computation resources (time and video storage space). We achieve this by balancing processing time and tracking accuracy by predicting the performance in a range of spatial resolutions. This approach can reduce the execution time by up to 34% with a slight decrease in performance of 3%.

Artifact Detection Maps Learned using Shallow Convolutional Networks

Abstract: Automatically identifying the locations and severities of video artifacts is a difficult problem. We have developed a general method for detecting local artifacts by learning differences between distorted and pristine video frames. Our model, which we call the Video Impairment Mapper (VID-MAP), produces a full resolution map of artifact detection probabilities based on comparisons of exitatory and inhibatory convolutional responses. Validation on a large database shows that our method outperforms the previous state-of-the-art. A software release of VID-MAP that was trained to produce upscaling and combing detection probability maps is available online: http://live.ece.utexas.edu/research/quality/VIDMAP release.zip for public use and evaluation.

Breaking down the problem of blind video quality evaluation

Abstract: Although a variety of successful no-reference (blind) picture quality analyzers have been proposed, progress on the blind video quality analysis problem has been slow. We break down the problem of perceptual blind video quality assessment (VQA) into components, which we address individually, before proposing a holistic solution. The idea is to tackle the challenges that comprise the blind VQA problem individually in order to gain a better understanding of it. Publisher’s Note: The first printing of this volume was completed prior to the SPIE Digital Library publication and this paper has since been replaced with a corrected/revised version.

Quality Assessment of Mobile Videos with In-Capture Distortions

Abstract: We designed and created a new video database that models a variety of complex distortions generated during the video capturing process on hand-held mobile capturing devices. We describe the content and characteristics of the new database, which we call the LIVE Mobile In-Capture Video Quality Database. It comprises a total of 208 videos that were captured using eight different smart-phones and were affected by six common in-capture distortions. We also conducted a subjective video quality assessment study using this data, wherein each video was assessed by 36 unique subjects. We evaluated several top-performing No-Reference IQA and VQA algorithms on the new database and find insights on how real-world in-capture distortions challenge both human subjects as well as automatic perceptual quality prediction models.

Method of ranked residuals for binary texture filtering of additive symmetric noise

Abstract: Textures are degraded by Gaussian noise in the process of image acquisition. The restoration of a texture is very important for later texture analysis and classification. In this paper, the method of ranked residuals is proposed to restore binary texture which is corrupted by Gaussian noise. This method not only deletes the noise but also preserves all details of a texture. In addition, it has the property of preserving any line endings (not necessarily straight) and any boundary (concave or convex) at any orientation, edges, and corners. The main idea of ranked residual method is that it selects the windowed pixels that are closest to the windowed central value as the subset and chooses an estimator (median, mean, LMS, etc.) to estimate the central value. This allows us to adapt our choice of subsets. Therefore whatever the shape of texture looks like, the filter can preserve the texture detail and eliminate the noise at the same time. Some synthetic and real textures are used to demonstrate the properties of this filter.

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