S.H. Oguz

Bio: S.H. Oguz is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Transform coding & Discrete cosine transform. The author has an hindex of 2, co-authored 4 publications receiving 81 citations.

Image coding ringing artifact reduction using morphological post-filtering

Abstract: Ringing is an annoying artifact frequently encountered in low bit-rate transform and subband decomposition based compression of different media such as image, intra frame video and graphics. A mathematical morphology based post-processing algorithm is presented in this paper for image ringing artifact suppression. First, we use binary morphological operators to isolate the regions of an image where the ringing artifact is most prominent to the human visual system (HVS) while preserving genuine edges and other (high-frequency) fine details present in the image. Then, a gray-level morphological nonlinear smoothing filter is applied to the unmasked regions of the image under the filtering mask to eliminate ringing within this constraint region. To gauge the effectiveness of this approach, we propose an HVS compatible objective measure of the ringing artifact. Preliminary simulations indicate that the proposed method is capable of significantly reducing the ringing artifact on both subjective and objective basis.

Critical quantization decisions in transform coding and blocking artifacts

Abstract: In this work, a mechanism contributing to the blocking artifacts of standard block based transform coding schemes is stated and a simple solution is proposed for reducing its effect. The proposed solution can be easily incorporated into the encoding algorithms of existing two dimensional (2-D) discrete cosine transform (DCT) based image and video coding standards (JPEG, MPEG-1 and 2, H-261, H-263) without the need for a modification in their bitstream syntax.

A Modified MPEG-1 system based on genlot

Abstract: In this study, a modification to ISO MPEG-1 and MPEG-2 digital video coding standards is proposed and preliminary results on its performance are reported. The proposed modification aims to improve the visual quality of MPEG-1 and MPEG-2 coding at medium-to-low bit-rate regimes by eliminating the blocking effect caused by the Discrete Cosine Transform. This goal is achieved without introducing a significant change in the MPEG hierarchy and algorithm. The theory of Lapped Orthogonal Transforms which constitutes a rather recently introduced tool for block transform coding suggests that they can reduce the blocking effect to very low levels. Hence, in the modified MPEG-like system, instead of the original two dimensional Discrete Cosine Transform a Lapped Orthogonal Transformation is used as the basic spatial correlation reduction operation and also customized quantization and variable length codeword tables are provided to ensure efficiency. The modified coding algorithm is implemented in software. Simulations are made to compare its performance to the original MPEG-1 algorithm. As performance criteria, PSNR versus compression ratio (equivalently bit-rate) plots and also subjective ratings of visual quality are used.

Lost transform domain data concealment in GenLOT based coders

Abstract: In this paper, the problem of concealing lost transform domain data generated by a generalized lapped orthogonal transform (GenLOT) based image/video codec is considered. Based on the special structure of GenLOTs, we propose two computationally simple restoration algorithms one in the spatial domain, and the other in the transform domain, for the concealment of lost GenLOT coded data. Numeric and visual simulation results are provided for both algorithms along with their comparison to previous work.

Modern image quality assessment

Abstract: This book is about objective image quality assessmentwhere the aim is to provide computational models that can automatically predict perceptual image quality. The early years of the 21st century have witnessed a tremendous growth in the use of digital images as a means for representing and communicating information. A considerable percentage of this literature is devoted to methods for improving the appearance of images, or for maintaining the appearance of images that are processed. Nevertheless, the quality of digital images, processed or otherwise, is rarely perfect. Images are subject to distortions during acquisition, compression, transmission, processing, and reproduction. To maintain, control, and enhance the quality of images, it is important for image acquisition, management, communication, and processing systems to be able to identify and quantify image quality degradations. The goals of this book are as follows; a) to introduce the fundamentals of image quality assessment, and to explain the relevant engineering problems, b) to give a broad treatment of the current state-of-the-art in image quality assessment, by describing leading algorithms that address these engineering problems, and c) to provide new directions for future research, by introducing recent models and paradigms that significantly differ from those used in the past. The book is written to be accessible to university students curious about the state-of-the-art of image quality assessment, expert industrial R&D engineers seeking to implement image/video quality assessment systems for specific applications, and academic theorists interested in developing new algorithms for image quality assessment or using existing algorithms to design or optimize other image processing applications.

No-reference quality assessment using natural scene statistics: JPEG2000

Abstract: Measurement of image or video quality is crucial for many image-processing algorithms, such as acquisition, compression, restoration, enhancement, and reproduction. Traditionally, image quality assessment (QA) algorithms interpret image quality as similarity with a "reference" or "perfect" image. The obvious limitation of this approach is that the reference image or video may not be available to the QA algorithm. The field of blind, or no-reference, QA, in which image quality is predicted without the reference image or video, has been largely unexplored, with algorithms focussing mostly on measuring the blocking artifacts. Emerging image and video compression technologies can avoid the dreaded blocking artifact by using various mechanisms, but they introduce other types of distortions, specifically blurring and ringing. In this paper, we propose to use natural scene statistics (NSS) to blindly measure the quality of images compressed by JPEG2000 (or any other wavelet based) image coder. We claim that natural scenes contain nonlinear dependencies that are disturbed by the compression process, and that this disturbance can be quantified and related to human perceptions of quality. We train and test our algorithm with data from human subjects, and show that 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.

No-reference image and video quality estimation: Applications and human-motivated design

Abstract: This paper reviews the basic background knowledge necessary to design effective no-reference (NR) quality estimators (QEs) for images and video. We describe a three-stage framework for NR QE that encompasses the range of potential use scenarios for the NR QE and allows knowledge of the human visual system to be incorporated throughout. We survey the measurement stage of the framework, considering methods that rely on bitstream, pixels, or both. By exploring both the accuracy requirements of potential uses as well as evaluation criteria to stress-test a QE, we set the stage for our community to make substantial future improvements to the challenging problem of NR quality estimation.

dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs

Abstract: Objective assessment of image quality is fundamentally important in many image processing tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models, which predict the quality of a digital image with no access to its original pristine-quality counterpart as reference. One of the biggest challenges in learning BIQA models is the conflict between the gigantic image space (which is in the dimension of the number of image pixels) and the extremely limited reliable ground truth data for training. Such data are typically collected via subjective testing, which is cumbersome, slow, and expensive. Here, we first show that a vast amount of reliable training data in the form of quality-discriminable image pairs (DIPs) can be obtained automatically at low cost by exploiting large-scale databases with diverse image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no subjective opinions are used for training) model using RankNet, a pairwise learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index. Extensive experiments on four benchmark IQA databases demonstrate that dipIQ outperforms the state-of-the-art OU-BIQA models. The robustness of dipIQ is also significantly improved as confirmed by the group MAximum Differentiation competition method. Furthermore, we extend the proposed framework by learning models with ListNet (a listwise L2R algorithm) on quality-discriminable image lists (DIL). The resulting DIL inferred quality index achieves an additional performance gain.

A No-Reference Metric for Perceived Ringing Artifacts in Images

Abstract: A novel no-reference metric that can automatically quantify ringing annoyance in compressed images is presented. In the first step a recently proposed ringing region detection method extracts the regions which are likely to be impaired by ringing artifacts. To quantify ringing annoyance in these detected regions, the visibility of ringing artifacts is estimated, and is compared to the activity of the corresponding local background. The local annoyance score calculated for each individual ringing region is averaged over all ringing regions to yield a ringing annoyance score for the whole image. A psychovisual experiment is carried out to measure ringing annoyance subjectively and to validate the proposed metric. The performance of our metric is compared to existing alternatives in literature and shows to be highly consistent with subjective data.