Shanghai Key Laboratory of Digital Media Processing and Transmissions

Shanghai, China

Shanghai Key Laboratory of Digital Media Processing and Transmissions

Shanghai, China
SEARCH FILTERS
Time filter
Source Type

Zhou J.,Shanghai JiaoTong University | Zhou J.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | Gu X.,Shanghai JiaoTong University | Gu X.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | And 2 more authors.
IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB | Year: 2017

Stereoscopic 3D (S3D) image technology has been extensively developed in the last decades. Visual discomfort such as eye strain, headache, fatigue, asthenopia, and other phenomena leading to a less pleasant viewing experience is still a potential issue in S3D applications. How to evaluate S3D image quality that related to visual discomfort is still a challenging problem. A larger number of studies have been done on S3D Image Quality Assessment (S3D IQA) where the subjective assessed S3D Image Databases play an important role. The subjective scores were collected for each S3D image in database with a number of viewers. Usually, Likert scale is adopted for observers to mark their subjective quality score, and then mean opinion score (MOS) is estimated. Due to the law of comparative judgment, the quality of subjective scores varies among observers and depends on the judgment method. This paper studied the quality of two subjective assessment methodologies-single stimulus (SS) and pairwise comparison (PC). Considering the S3D IQA as a S3D images' quality ranking problem, we applied single stimulus and pairwise comparison subjective testing on a set of S3D images with known geometric distortions. From SS subjective testing results, the S3D images' ranking can be derived by sorting MOSs directly. From PC subjective testing results, the ranking can be derived from DMOS scores. The distorted S3D images can be ranked via their geometric distortion parameters. The quality of subjective assessed results from SS and PC are then evaluated on the correlation between their ranking results to corresponding geometric distortions. With the collected MOSs for geometric distorted S3D image database, a deep-learning based S3D IQA model was used to study the relationship between the model performance and the quality of subjective assessment. © 2017 IEEE.


Gu K.,Shanghai JiaoTong University | Gu K.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | Zhai G.,Shanghai JiaoTong University | Zhai G.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | And 4 more authors.
2014 IEEE International Conference on Image Processing, ICIP 2014 | Year: 2014

This paper investigates the problem of full-reference (FR) image quality assessment (IQA). In general, the ideal IQA metric should be effective and efficient, yet most of existing FR IQA methods cannot reach these two targets simultaneously. Under the supposition that the human visual perception to image quality depends on salient local distortion and global quality degradation, we introduce a novel effective and efficient local-tuned-global (LTG) model induced IQA metric. Extensive experiments are conducted on five publicly available subject-rated color image quality databases, including LIVE, TID2008, CSIQ, IVC and TID2013, to evaluate and compare our algorithm with classical and state-of-the-art FR IQA approaches. The proposed LTG is shown to work fast and outperform those competing methods. © 2014 IEEE.


Gu K.,Shanghai JiaoTong University | Gu K.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | Zhai G.,Shanghai JiaoTong University | Zhai G.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | And 4 more authors.
Proceedings - IEEE International Symposium on Circuits and Systems | Year: 2013

Image quality assessment (IQA) is an important research area in image processing. Reduced-reference (RR) IQA methods contained therein mainly aim to estimate image quality degradations with partial information about the reference image. Following the remarkable achievement of SSIM, structural information has been recognized as one key factor, and has aroused many image quality metrics so far. In this paper, we design a structural degradation model (SDM). Then, the quality score of an image is defined as a nonlinear combination, or SVM based integration, of distance between the structural degradation information of the original and distorted images. Accordingly, a new RR IQA approach using the SDM model is exploited. Experimental results on LIVE database are provided to justify the superior prediction accuracy performance of the proposed method as compared to three significant image quality metrics, PSNR, SSIM and FEDM. © 2013 IEEE.


Gu K.,Shanghai JiaoTong University | Gu K.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | Zhai G.,Shanghai JiaoTong University | Zhai G.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | And 4 more authors.
Proceedings - IEEE International Symposium on Circuits and Systems | Year: 2013

Recently, an increasing number of image quality assessment (IQA) algorithms have been developed based on multi-scale methods, such as MS-SSIM, IFC, VIF and IW-PSNR/SSIM. Inspired by the achievement of multi-scale type of IQA algorithms, this paper proposes a self-adaptive scale transform based IQA approach. Using image size and viewing distance as input variables, we construct a self-adaptive scale transform function to estimate the suitable scale transform coefficient for the following image quality metrics. Two of the most well-known full-reference IQA methods (PSNR and SSIM), and three publicly-available subjectrated image databases (LIVE, IVC and Toyama-MICT) with clear image size and viewing distance values are used as testing beds in this paper. Experimental results and comparative studies on different combinations of IQA methods and image databases suggest the effectiveness and the robustness of the proposed approach. © 2013 IEEE.


Gu K.,Shanghai JiaoTong University | Gu K.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | Zhai G.,Shanghai JiaoTong University | Zhai G.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | And 5 more authors.
Proceedings - IEEE International Conference on Multimedia and Expo | Year: 2013

In the research of image quality assessment (IQA), no-reference approaches are usually thought of as a big challenge since none of original image information is available. To tackle this problem, we propose a new no-reference image quality metric through combining two recently proposed reduced-reference IQA models, namely the free energy based distortion metric (FEDM) and the structural degradation model (SDM). In this work, it will be shown that there exists an approximate linear relationship between the original image information of the free energy feature and the structural degradation information. Based on this observation and the application of support vector machine (SVM) that is widely used in the current study of IQA, our newly developed No-reference Free energy and Structural degradation based Distortion Metric (NFSDM) is found to alleviate the dependance of original images, and has achieved remarkably well prediction accuracy, outperforming the most two full-reference IQA approaches PSNR/SSIM and several mainstream no-reference image quality metrics. © 2013 IEEE.


Gu K.,Shanghai JiaoTong University | Gu K.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | Zhai G.,Shanghai JiaoTong University | Zhai G.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | And 6 more authors.
2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings | Year: 2013

It is widely known that, for most natural images, appropriate contrast enhancement can usually lead to improved subjective quality. Despite of its importance to image processing, contrast change has largely been overlooked in the current research of image quality assessment (IQA). To fill this void, in this paper we first report a new and dedicated contrast-changed image database (CID2013). The CID2013 database is composed of four hundred contrast-changed images of fifteen original natural images and the mean opinion scores (MOSs) recorded from twenty-two inexperienced viewers. We then proposed a novel reduced-reference image quality metric for contrast-changed images (RIQMC) using entropies and order statistics of the image histograms. Experimental results on the CID2013, TID2008, and CSIQ databases demonstrate that the proposed RIQMC metric outperforms some mainstream image quality assessment methods. © 2013 IEEE.


Li N.,Shanghai JiaoTong University | Li N.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | Xu Y.,Shanghai JiaoTong University | Xu Y.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | And 2 more authors.
2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 | Year: 2010

Gait recognition has already achieved satisfactory performance on small databases under ideal conditions. Most of the existing approaches represent gait pattern using a locomotion model or statistic model of human silhouette. However, it is still a challenging task to conduct human gait identification under variations of clothing and carrying condition in real scenes. In this paper, an adaptive part-based feature selection method is proposed to filter out interference feature blocks and a matching procedure is performed to identify the correct subject. Compared with the state-of-the-art methods on a large standard dataset, the proposed method shows an encouraging computational complexity reduction and performance improvement in identification rates. © 2010 IEEE.


Gong M.,Institute of Image Communication and Information Processing | Gong M.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | Xu Y.,Institute of Image Communication and Information Processing | Xu Y.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | And 4 more authors.
Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011 | Year: 2011

Gait recognition under variations of clothing and carrying condition is still a challenging task. In this paper, we present a gait identification method via sparse representation. We formulate the recognition problem as finding the coefficients of linear combination of the training samples plus an error term and discuss sparse signal representation theory that offers the solution to this problem. Based on the sparse representation computed by l1-minimization, we define a new distance metric to choose non-polluted area and propose a method for gait identification. Compared with the state-of-the-art methods on a large dataset, the proposed method achieves significant performance improvement in identification rates and it shows robustness to variations. © 2011 IEEE.


Gu K.,Shanghai JiaoTong University | Gu K.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | Zhai G.,Shanghai JiaoTong University | Zhai G.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | And 6 more authors.
2012 IEEE 14th International Workshop on Multimedia Signal Processing, MMSP 2012 - Proceedings | Year: 2012

Most state-of-the-art image quality metrics are based on the two-step approach: local distortion/fidelity measurement and pooling. During the pooling stage, many weighting strategies have been proposed incorporating properties of the distortion itself, various masking effects and visual attention. Recently, researchers have devoted great enthusiasm and effort to the improvement of image quality assessment using visual saliency models. In this research, it is noticed that visual saliency features of both the original image and the distorted one have impacts on the process of image quality assessment. To reduce the overlapping effects, a nonlinear additive model is proposed to integrate saliency features from the original and distorted images towards improved error weighting results. Our extensive experimental studies on four publicly available image databases (LIVE, TID2008, CSIQ and A57) indicate that the proposed improved nonlinear additive model based saliency map weighting strategy constantly leads to higher prediction accuracy for image quality assessment than traditional methods. © 2012 IEEE.


Gu K.,Shanghai JiaoTong University | Gu K.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | Zhai G.,Shanghai JiaoTong University | Zhai G.,Shanghai Key Laboratory of Digital Media Processing and Transmissions | And 4 more authors.
Signal, Image and Video Processing | Year: 2013

This paper investigates the impacts of image quality level on the prediction accuracy of image quality metrics. While many state-of-the-art perceptual image quality assessment methods have achieved fairly well performances in terms of the correlation between the quality predictions and the subjective scores, none of them took into account the effects of the quality levels of those test images on prediction accuracy of the quality metrics. In this work, inspired by the mechanism of human perception under high- and low-quality conditions, we propose a new image quality assessment paradigm based on image quality level classification. Our investigation on TID2008 and other three publicly available databases (LIVE, CSIQ and Toyama-MICT) results in two valuable findings. First, the performances of major well-known image quality assessment methods are significantly affected by image quality level. Second, through combining different quality metrics for different quality levels, superior performance can be achieved as compared to some of the best image quality metrics, e. g., SSIM, MS-SSIM, VIF and VIFP. Experiments and comparative studies are provided to confirm the effectiveness of the proposed new paradigm by differentiating quality levels for image quality assessment. © 2013 Springer-Verlag London.

Loading Shanghai Key Laboratory of Digital Media Processing and Transmissions collaborators
Loading Shanghai Key Laboratory of Digital Media Processing and Transmissions collaborators