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Ee Ping Ong

Bio: Ee Ping Ong is an academic researcher from Agency for Science, Technology and Research. The author has contributed to research in topics: Video quality & Image quality. The author has an hindex of 24, co-authored 86 publications receiving 2160 citations. Previous affiliations of Ee Ping Ong include Institute for Infocomm Research Singapore.


Papers
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Journal ArticleDOI
TL;DR: A new perceptually-adaptive video coding (PVC) scheme for hybrid video compression is explored, in order to achieve better perceptual coding quality and operational efficiency and to integrate spatial masking factors with the nonlinear additivity model for masking (NAMM).
Abstract: We explore a new perceptually-adaptive video coding (PVC) scheme for hybrid video compression, in order to achieve better perceptual coding quality and operational efficiency. A new just noticeable distortion (JND) estimator for color video is first devised in the image domain. How to efficiently integrate masking effects together is a key issue of JND modelling. We integrate spatial masking factors with the nonlinear additivity model for masking (NAMM). The JND estimator applies to all color components and accounts for the compound impact of luminance masking, texture masking and temporal masking. Extensive subjective viewing confirms that it is capable of determining a more accurate visibility threshold that is close to the actual JND bound in human eyes. Secondly, the image-domain JND profile is incorporated into hybrid video encoding via the JND-adaptive motion estimation and residue filtering process. The scheme works with any prevalent video coding standards and various motion estimation strategies. To demonstrate the effectiveness of the proposed scheme, it has been implemented in the MPEG-2 TM5 coder and demonstrated to achieve average improvement of over 18% in motion estimation efficiency, 0.6 dB in average peak signal-to perceptual-noise ratio (PSPNR) and most remarkably, 0.17 dB in the objective coding quality measure (PSNR) on average. Theoretical explanation is presented for the improvement on the objective coding quality measure. With the JND-based motion estimation and residue filtering process, hybrid video encoding can be more efficient and the use of bits is optimized for visual quality.

305 citations

Journal ArticleDOI
TL;DR: A new numerical measure for visual attention's modulatory aftereffects, perceptual quality significance map (PQSM), is proposed and demonstrates the performance improvement on two PQSM-modulated visual sensitivity models and two P QSM-based visual quality metrics.
Abstract: With the fast development of visual noise-shaping related applications (visual compression, error resilience, watermarking, encryption, and display), there is an increasingly significant demand on incorporating perceptual characteristics into these applications for improved performance. In this paper, a very important mechanism of the human brain, visual attention, is introduced for visual sensitivity and visual quality evaluation. Based upon the analysis, a new numerical measure for visual attention's modulatory aftereffects, perceptual quality significance map (PQSM), is proposed. To a certain extent, the PQSM reflects the processing ability of the human brain on local visual contents statistically. The PQSM is generated with the integration of local perceptual stimuli from color contrast, texture contrast, motion, as well as cognitive features (skin color and face in this study). Experimental results with subjective viewing demonstrate the performance improvement on two PQSM-modulated visual sensitivity models and two PQSM-based visual quality metrics.

194 citations

Journal ArticleDOI
TL;DR: A new JND estimator for color video is devised in image-domain with the nonlinear additivity model for masking and is incorporated into a motion-compensated residue signal preprocessor for variance reduction toward coding quality enhancement, and both perceptual quality and objective quality are enhanced in coded video at a given bit rate.
Abstract: We present a motion-compensated residue signal preprocessing scheme in video coding scheme based on just-noticeable-distortion (JND) profile Human eyes cannot sense any changes below the JND threshold around a pixel due to their underlying spatial/temporal masking properties An appropriate (even imperfect) JND model can significantly help to improve the performance of video coding algorithms From the viewpoint of signal compression, smaller variance of signal results in less objective distortion of the reconstructed signal for a given bit rate In this paper, a new JND estimator for color video is devised in image-domain with the nonlinear additivity model for masking (NAMM) and is incorporated into a motion-compensated residue signal preprocessor for variance reduction toward coding quality enhancement As the result, both perceptual quality and objective quality are enhanced in coded video at a given bit rate A solution of adaptively determining the parameter for the residue preprocessor is also proposed The devised technique can be applied to any standardized video coding scheme based on motion compensated prediction It provides an extra design option for quality control, besides quantization, in contrast with most of the existing perceptually adaptive schemes which have so far focused on determination of proper quantization steps As an example for demonstration, the proposed scheme has been implemented in the MPEG-2 TM5 coder, and achieved an average peak signal-to-noise (PSNR) increment of 0505 dB over the twenty video sequences which have been tested The perceptual quality improvement has been confirmed by the subjective viewing tests conducted

191 citations

Proceedings ArticleDOI
01 Jul 2003
TL;DR: The proposed method for measuring the perceptual quality of blurred images can provide results that correlate relatively well with human subjective ratings, and the effectiveness of such method is validated using subjective tests on blurred images.
Abstract: In this paper, a method for measuring the perceptual quality of blurred images has been proposed. Here, the amount of image blur is characterized by the average extent of edges in the image, or more specifically the average extent of the slope's spread of an edge in the opposing gradients' directions. The effectiveness of such method is validated using subjective tests on blurred images, including JPEG-2000 coded images, and the experimental results show that the proposed method can provide results that correlate relatively well with human subjective ratings.

151 citations

Journal ArticleDOI
TL;DR: Both objective and subjective quality evaluations are given by evaluating the proposed perceptual rate control (PRC) scheme in the H.263 platform, and the evaluations show that the proposed PRC scheme achieves significant quality improvement in block-based coding for bandwidth-hungry applications.
Abstract: We present a method for extracting local visual perceptual cues and its application for rate control of videophone, in order to ensure the scarce bits to be assigned for maximum perceptual coding quality. The optimum quantization step is determined with the rate-distortion model considering the local perceptual cues in the visual signal. For extraction of the perceptual cues, luminance adaptation and texture masking are used as the stimulus-driven factors, while skin color serves as the cognition-driven factor in the current implementation. Both objective and subjective quality evaluations are given by evaluating the proposed perceptual rate control (PRC) scheme in the H.263 platform, and the evaluations show that the proposed PRC scheme achieves significant quality improvement in block-based coding for bandwidth-hungry applications.

111 citations


Cited by
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Journal ArticleDOI
TL;DR: This article has reviewed the reasons why people want to love or leave the venerable (but perhaps hoary) MSE and reviewed emerging alternative signal fidelity measures and discussed their potential application to a wide variety of problems.
Abstract: In this article, we have reviewed the reasons why we (collectively) want to love or leave the venerable (but perhaps hoary) MSE. We have also reviewed emerging alternative signal fidelity measures and discussed their potential application to a wide variety of problems. The message we are trying to send here is not that one should abandon use of the MSE nor to blindly switch to any other particular signal fidelity measure. Rather, we hope to make the point that there are powerful, easy-to-use, and easy-to-understand alternatives that might be deployed depending on the application environment and needs. While we expect (and indeed, hope) that the MSE will continue to be widely used as a signal fidelity measure, it is our greater desire to see more advanced signal fidelity measures being used, especially in applications where perceptual criteria might be relevant. Ideally, the performance of a new signal processing algorithm might be compared to other algorithms using several fidelity criteria. Lastly, we hope that we have given further motivation to the community to consider recent advanced signal fidelity measures as design criteria for optimizing signal processing algorithms and systems. It is in this direction that we believe that the greatest benefit eventually lies.

2,601 citations

Proceedings Article
01 Jan 1999

2,010 citations

Journal ArticleDOI
TL;DR: DIIVINE is capable of assessing the quality of a distorted image across multiple distortion categories, as against most NR IQA algorithms that are distortion-specific in nature, and is statistically superior to the often used measure of peak signal-to-noise ratio (PSNR) and statistically equivalent to the popular structural similarity index (SSIM).
Abstract: Our approach to blind image quality assessment (IQA) is based on the hypothesis that natural scenes possess certain statistical properties which are altered in the presence of distortion, rendering them un-natural; and that by characterizing this un-naturalness using scene statistics, one can identify the distortion afflicting the image and perform no-reference (NR) IQA. Based on this theory, we propose an (NR)/blind algorithm-the Distortion Identification-based Image Verity and INtegrity Evaluation (DIIVINE) index-that assesses the quality of a distorted image without need for a reference image. DIIVINE is based on a 2-stage framework involving distortion identification followed by distortion-specific quality assessment. DIIVINE is capable of assessing the quality of a distorted image across multiple distortion categories, as against most NR IQA algorithms that are distortion-specific in nature. DIIVINE is based on natural scene statistics which govern the behavior of natural images. In this paper, we detail the principles underlying DIIVINE, the statistical features extracted and their relevance to perception and thoroughly evaluate the algorithm on the popular LIVE IQA database. Further, we compare the performance of DIIVINE against leading full-reference (FR) IQA algorithms and demonstrate that DIIVINE is statistically superior to the often used measure of peak signal-to-noise ratio (PSNR) and statistically equivalent to the popular structural similarity index (SSIM). A software release of DIIVINE has been made available online: http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip for public use and evaluation.

1,501 citations

Journal ArticleDOI
TL;DR: In this paper, the authors compare the role of smoothing/regularization processes that are required in local and global differential methods for optic flow computation, and propose a simple confidence measure that minimizes energy functionals.
Abstract: Differential methods belong to the most widely used techniques for optic flow computation in image sequences. They can be classified into local methods such as the Lucas-Kanade technique or Bigun's structure tensor method, and into global methods such as the Horn/Schunck approach and its extensions. Often local methods are more robust under noise, while global techniques yield dense flow fields. The goal of this paper is to contribute to a better understanding and the design of novel differential methods in four ways: (i) We juxtapose the role of smoothing/regularisation processes that are required in local and global differential methods for optic flow computation. (ii) This discussion motivates us to describe and evaluate a novel method that combines important advantages of local and global approaches: It yields dense flow fields that are robust against noise. (iii) Spatiotemporal and nonlinear extensions as well as multiresolution frameworks are presented for this hybrid method. (iv) We propose a simple confidence measure for optic flow methods that minimise energy functionals. It allows to sparsify a dense flow field gradually, depending on the reliability required for the resulting flow. Comparisons with experiments from the literature demonstrate the favourable performance of the proposed methods and the confidence measure.

1,256 citations

Journal ArticleDOI
TL;DR: This paper aims to test the hypothesis that when viewing natural images, the optimal perceptual weights for pooling should be proportional to local information content, which can be estimated in units of bit using advanced statistical models of natural images.
Abstract: Many state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage structure: local quality/distortion measurement followed by pooling. While significant progress has been made in measuring local image quality/distortion, the pooling stage is often done in ad-hoc ways, lacking theoretical principles and reliable computational models. This paper aims to test the hypothesis that when viewing natural images, the optimal perceptual weights for pooling should be proportional to local information content, which can be estimated in units of bit using advanced statistical models of natural images. Our extensive studies based upon six publicly-available subject-rated image databases concluded with three useful findings. First, information content weighting leads to consistent improvement in the performance of IQA algorithms. Second, surprisingly, with information content weighting, even the widely criticized peak signal-to-noise-ratio can be converted to a competitive perceptual quality measure when compared with state-of-the-art algorithms. Third, the best overall performance is achieved by combining information content weighting with multiscale structural similarity measures.

1,147 citations