Joe Yuchieh Lin

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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(More)
A high-definition video quality assessment (VQA) database that captures two typical video distortion types in video services (namely, ‘‘compression’’ and ‘‘compression followed by scaling’’) is presented in this work. The VQA database, called MCL-V, contains 12 source video clips and 96 distorted video clips with subjective assessment scores. The source(More)
The Netflix ingest and encoding pipeline is a cloud-based platform that generates video encodes for the Netflix streaming service. Due to the large throughput of the system, automated video quality assessment of the source videos and the generated encodes is essential in ensuring the quality of experience of viewers. This paper discusses the motivations for(More)
A compressed video quality assessment dataset based on the just noticeable difference (JND) model, called MCL-JCV, is recently constructed and released. In this work, we explain its design objectives, selected video content and subject test procedures. Then, we conduct statistical analysis on collected JND data. We compute the difference between every two(More)
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(More)
A full-reference video quality assessment (VQA) method, called the ensemble-learning-based video quality assessment (EVQA) index, is proposed in this work. As compared with previous learning-based VQA methods, it has two unique features. First, EVQA adopts a frame-based learning mechanism to address the limited training data problem. Second, a dynamic image(More)
Modern image quality assessment (IQA) indices, e.g. SSIM and FSIM, are proved to be effective for some image distortion types. However, they do not exploit the characteristics of the human visual system (HVS) explicitly. In this work, we investigate a method to incorporate the human visual saliency (VS) model in these full-reference indices, and call the(More)
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