Digital coding of waveforms: Principles and applications to speech and video

Accurate measurement of the perceived quality of audio-visual services at the end-user is becoming a crucial issue in digital applications due to the growing demand for compression and transmission of audio-visual services over communication networks. Content providers strive to offer the best quality of experience for customers linked to their different quality of service (QoS) solutions. Therefore, developing accurate, perceptual-based quality metrics is a key requirement in multimedia services. In this paper, we survey state-of-the-art signal-driven perceptual audio and video quality assessment methods independently, and investigate relevant issues in developing joint audio-visual quality metrics. Experiments with respect to subjective quality results have been conducted for analyzing and comparing the performance of the quality metrics. We consider emerging trends in audio-visual quality assessment, and propose feasible solutions for future work in perceptual-based audio-visual quality metrics.

Perceptual-based quality assessment for audio-visual services: A survey

A modification of the new edge-directed interpolation method is presented. The modification eliminates the prediction error accumulation problem with adopting a modified training window structure, and further extends the covariance matching into multiple directions for suppressing the covariance mis-match problem. Simulation results show that the proposed method achieves remarkable subjective performance in preserving the edge smoothness and sharpness among other methods in literature. It also demonstrates consistent objective performance among a variety of images.

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A Modified Edge Directed Interpolation for images

In this paper, we show how the proposed model in ITU-T Recommendation G.1070 "Opinion model for video-telephony applications" cannot model properly the perceptual video quality, especially in the low bit rate range, due to the great variation of MOS values depending on video content. In this work, we present different enhancements to the model, allowing a much better approximation to the perceptual MOS values, knowing only the subjective movement content in the video application, classified in "Low", "Medium" or "High". Studies were made for more than 1500 processed video clips, coded in MPEG-2 and H.264/AVC, in bit rate ranges from 50 kb/s to 12 Mb/s, in SD, VGA, CIF and QCIF display formats. Video clips subjective quality was estimated using one of the quality metrics standardized in ITU-T Recommendation J.144 and ITU-R Recommendation BT.1683.

Enhancements to the opinion model for video-telephony applications

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.

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

We propose a new method for an objective measurement of video quality. By analyzing subjective scores of various video sequences, we find that the human visual system is particularly sensitive to degradation around edges. In other words, when edge areas of a video sequence are degraded, evaluators tend to give low quality scores to the video, even though the overall mean squared error is not large. Based on this observation, we propose an objective video quality measurement method based on degradation around edges. In the proposed method, we first apply an edge detection algorithm to videos and locate edge areas. Then, we measure degradation of those edge areas by computing mean squared errors and use it as a video quality metric after some postprocessing. Experiments show that the proposed method significantly outperforms the conventional peak signal-to-noise ratio (PSNR). This method was also independently evaluated by independent laboratory groups in the Video Quality Experts Group (VQEG) Phase 2 test. The method consistently provided good performances. As a result, the method was included in international recommendations for objective video quality measurement.

Objective video quality assessment

In this paper, we propose a new method for an objective measurement of video quality based on edge degradation. One of the most important requirements for an objective method for video quality measurement is that it should provide consistent performances over a wide range of video sequences that are not used in the designing stage. By analyzing subjective scores of various video sequences, we found that the human visual system is sensitive to degradation around edges. In other words, when edge areas of a video are blurred, evaluators tend to give low scores to the video even though the overall mean squared error is not so large. Based on this observation, we propose an objective video quality measurement method that measures degradation around edges. In the proposed method, we first apply an edge detection algorithm to videos and find edge areas. Then, we measure degradation of those edge areas by computing mean squared error. From this mean squared error, we compute the PSNR and use it as video quality metric. Experimental results show that the proposed method compares favorably with the current objective methods for video quality measurement. Furthermore, when the proposed method is applied to test video sequences that are not used in the designing stage, it still consistently provides satisfactory performances.

Edge degradation for objective video quality metrics

In this paper, we investigate the performance of an objective video quality assessment method using the wavelet transform for a large data set. The objective video quality assessment utilizes the wavelet transform, which is applied to each frame of source and processed videos in order to compute spatial frequency components. Then, the difference (squared error) of the wavelet coefficients in each subband is computed and summed. By repeating this procedure to the entire frames of a video, a sequence of difference vectors and the average vector are obtained. Each component of the average vector represents a difference in a certain spatial frequency. In order to take into account the temporal frequencies, a modified 3-D wavelet transform can be applied. Although this evaluation method provides a good performance for training data, its performance for new test videos remains to be seen due to a large number of parameters. In this paper, we apply the method to a large video data set and analyze the performance.

Sungdeuk Cho

Papers

Objective video quality assessment

Edge degradation for objective video quality metrics

Analysis of objective video quality metric using wavelet transform