Tsung-Jung Liu

Bio: Tsung-Jung Liu is an academic researcher from National Chung Hsing University. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 15, co-authored 46 publications receiving 714 citations. Previous affiliations of Tsung-Jung Liu include University of Southern California & Arizona State University.

Image Quality Assessment Using Multi-Method Fusion

Abstract: A new methodology for objective image quality assessment (IQA) with multi-method fusion (MMF) is presented in this paper. The research is motivated by the observation that there is no single method that can give the best performance in all situations. To achieve MMF, we adopt a regression approach. The new MMF score is set to be the nonlinear combination of scores from multiple methods with suitable weights obtained by a training process. In order to improve the regression results further, we divide distorted images into three to five groups based on the distortion types and perform regression within each group, which is called “context-dependent MMF” (CD-MMF). One task in CD-MMF is to determine the context automatically, which is achieved by a machine learning approach. To further reduce the complexity of MMF, we perform algorithms to select a small subset from the candidate method set. The result is very good even if only three quality assessment methods are included in the fusion process. The proposed MMF method using support vector regression is shown to outperform a large number of existing IQA methods by a significant margin when being tested in six representative databases.

A ParaBoost Method to Image Quality Assessment

Abstract: An ensemble method for full-reference image quality assessment (IQA) based on the parallel boosting (ParaBoost) idea is proposed in this paper. We first extract features from existing image quality metrics and train them to form basic image quality scorers (BIQSs). Then, we select additional features to address specific distortion types and train them to construct auxiliary image quality scorers (AIQSs). Both BIQSs and AIQSs are trained on small image subsets of certain distortion types and, as a result, they are weak performers with respect to a wide variety of distortions. Finally, we adopt the ParaBoost framework, which is a statistical scorer selection scheme for support vector regression (SVR), to fuse the scores of BIQSs and AIQSs to evaluate the images containing a wide range of distortion types. This ParaBoost methodology can be easily extended to images of new distortion types. Extensive experiments are conducted to demonstrate the superior performance of the ParaBoost method, which outperforms existing IQA methods by a significant margin. Specifically, the Spearman rank order correlation coefficients (SROCCs) of the ParaBoost method with respect to the LIVE, CSIQ, TID2008, and TID2013 image quality databases are 0.98, 0.97, 0.98, and 0.96, respectively.

Visual quality assessment : recent developments, coding applications and future trends

Abstract: Research on visual quality assessment has been active during the last decade. In this work, we provide an in-depth review of recent developments in the field. As compared with existing survey papers, our current work has several unique contributions. First, besides image quality databases and metrics, we put equal emphasis on video quality databases and metrics as this is a less investigated area. Second, we discuss the application of visual quality evaluation to perceptual coding as an example for applications. Third, we benchmark the performance of state-of-the-art visual quality metrics with experiments. Finally, future trends in visual quality assessment are discussed.

A fusion-based video quality assessment (fvqa) index

Abstract: In this work, we study the visual quality of streaming video and propose a fusion-based video quality assessment (FVQA) index to predict its quality. In the first step, video sequences are grouped according to their content complexity to reduce content diversity within each group. Then, at the second step, several existing video quality assessment methods are fused to provide the final video quality score, where fusion coefficients are learned from training video samples in the same group. We demonstrate the superior performance of FVQA as compared with other video quality assessment methods using the MCL-V video quality database.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Statistical Pattern Recognition

Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Image database TID2013

Abstract: This paper describes a recently created image database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new database contains a larger number (3000) of test images obtained from 25 reference images, 24 types of distortions for each reference image, and 5 levels for each type of distortion. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted images are presented. Mean opinion scores (MOS) for the new database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA). The availability of MOS allows the use of the designed database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted images and specific image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created image database and the collected MOS values are freely available for downloading and utilization for scientific purposes. We have created a new large database.This database contains larger number of distorted images and distortion types.MOS values for all images are obtained and provided.Analysis of correlation between MOS and a wide set of existing metrics is carried out.Methodology for determining drawbacks of existing visual quality metrics is described.

Primer of Biostatistics

Abstract: This book attempts to achieve a difficult goal: to teach statistics to the novice so as to impart a liking and understanding of statistics. The book is geared toward a medical audience, since most examples are from the medical literature. The structure of the book consists of the following elements in each chapter: a small number of statistical rules of thumb, followed by a nontechnical explanation, a demonstration of how to work through the mechanics of doing the statistical test in question, a summary, and sample problems to be solved by the reader. (The answers, with explanations, are provided in an appendix.) Although a variety of univariate statistics are included, certain topics that are important in medical research are not. For example, there is little or no discussion of multiple regression, life-table techniques, or pooling of studies. These omissions, especially of multiple regression, are unfortunate. The Primer was derived from