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.

Multiresolution feature extraction based on multifrequency decomposition

Abstract: Edges and regions are important features for various computer vision and pattern recognition applications. We propose simple schemes for generating edge and region features based on multifrequency decomposition. These methods involve two steps. First, a multiresolution image structure is obtained by multifrequency decomposition. Second, multiresolution features are generated from this multiresolution image structure by some simple operations. Since these features have a hierarchical structure, we can use the coarse-to-fine strategy to improve the reliability and computational efficiency for matching and for other applications. >

A bound involving n-dimensional instantaneous frequency

Abstract: The author states and proves an integral inequality that bounds the absolute difference epsilon (x)= mod m(x) -m(x) mod where m(x) is the response of a modulated n-dimensional real linear filter w to a complex exponential signal with n-dimensional instantaneous frequency Delta u(x) and m(x)= mod W( Delta u(x)-u/sub 0/) mod where W is the Fourier transform of w. The quantity of epsilon (x) provides an estimate of the error incurred by using m(x) as an estimate of m(x), e.g., if Delta u(x) is unknown. Such estimates may be applied to the problem of measuring the n-dimensional instantaneous frequency of certain nonstationary phase-modulated signals. >

SAMScore: A Semantic Structural Similarity Metric for Image Translation Evaluation

Abstract: Image translation has wide applications, such as style transfer and modality conversion, usually aiming to generate images having both high degrees of realism and faithfulness. These problems remain difficult, especially when it is important to preserve semantic structures. Traditional image-level similarity metrics are of limited use, since the semantics of an image are high-level, and not strongly governed by pixel-wise faithfulness to an original image. Towards filling this gap, we introduce SAMScore, a generic semantic structural similarity metric for evaluating the faithfulness of image translation models. SAMScore is based on the recent high-performance Segment Anything Model (SAM), which can perform semantic similarity comparisons with standout accuracy. We applied SAMScore on 19 image translation tasks, and found that it is able to outperform all other competitive metrics on all of the tasks. We envision that SAMScore will prove to be a valuable tool that will help to drive the vibrant field of image translation, by allowing for more precise evaluations of new and evolving translation models. The code is available at https://github.com/Kent0n-Li/SAMScore.

Maximally flat bandwidth allocation for variable bit rate video

Abstract: When a variable bit rate (VBR) video bitstream is transmitted into a network, the channel utilization is dependent on the maximum peak value of the traffic pattern and improved by reducing the peak rate. A lower bound on the transmission rate is derived from the given traffic cells. Based on the transmission rate constraint, the optimal bandwidth that is needed to meet the delay requirement during an allocation interval is also derived. From the delay bound and the constraints, we demonstrate that the traffic has been made as flat as possible.

Segmentation and extraction of the spinal canal in sagittal MR images

Abstract: We propose a fully automated spine MRI image segmentation method based on an extension of the multiscale Singularity Index. Without any training on image patches and any user information or annotation, the proposed method relies on the properties of T2 weighted sequencing and the power of the multiscale Singularity Index. Our method detects and extracts the centerline of the human spine and segments the relevant part of the spinal body by combining the filter responses and discarding erroneous candidates for the spinal region. This segmentation step can be critical for further analysis of the spinal region either by a physician or an automated algorithm. Evaluation on a variety of spine images shows that this method achieves results comparable to human segmentations on many challenging instances.

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