Music Genre Classification Using Spectral Analysis and Sparse Representation of the Signals

In this paper, we proposed a robust music genre classification method based on a sparse FFT based feature extraction method which extracted with discriminating power of spectral analysis of non-stationary audio signals, and the capability of sparse representation based classifiers. Feature extraction method combines two sets of features namely short-term features (extracted from windowed signals) and long-term features (extracted from combination of extracted short-time features). Experimental results demonstrate that the proposed feature extraction method leads to a sparse representation of audio signals. As a result, a significant reduction in the dimensionality of the signals is achieved. The extracted features are then fed into a sparse representation based classifier (SRC). Our experimental results on the GTZAN database demonstrate that the proposed method outperforms the other state of the art SRC approaches. Moreover, the computational efficiency of the proposed method is better than that of the other Compressive Sampling (CS)-based classifiers

3D Video Quality Assessment

A key factor in designing 3D systems is to understand how different visual cues and distortions affect the perceptual quality of 3D video. The ultimate way to assess video quality is through subjective tests. However, subjective evaluation is time consuming, expensive, and in most cases not even possible. An alternative solution is objective quality metrics, which attempt to model the Human Visual System (HVS) in order to assess the perceptual quality. The potential of 3D technology to significantly improve the immersiveness of video content has been hampered by the difficulty of objectively assessing Quality of Experience (QoE). A no-reference (NR) objective 3D quality metric, which could help determine capturing parameters and improve playback perceptual quality, would be welcomed by camera and display manufactures. Network providers would embrace a full-reference (FR) 3D quality metric, as they could use it to ensure efficient QoE-based resource management during compression and Quality of Service (QoS) during transmission.Comment: PhD Thesis, UBC, 201

Introducing A Public Stereoscopic 3D High Dynamic Range (SHDR) Video Database

High Dynamic Range (HDR) displays and cameras are paving their ways through the consumer market at a rapid growth rate. Thanks to TV and camera manufacturers, HDR systems are now becoming available commercially to end users. This is taking place only a few years after the blooming of 3D video technologies. MPEG/ITU are also actively working towards the standardization of these technologies. However, preliminary research efforts in these video technologies are hammered by the lack of sufficient experimental data. In this paper, we introduce a Stereoscopic 3D HDR (SHDR) database of videos that is made publicly available to the research community. We explain the procedure taken to capture, calibrate, and post-process the videos. In addition, we provide insights on potential use-cases, challenges, and research opportunities, implied by the combination of higher dynamic range of the HDR aspect, and depth impression of the 3D aspect

3D Video Quality Metric for 3D Video Compression

As the evolution of multiview display technology is bringing glasses-free 3DTV closer to reality, MPEG and VCEG are preparing an extension to HEVC to encode multiview video content. View synthesis in the current version of the 3D video codec is performed using PSNR as a quality metric measure. In this paper, we propose a full- reference Human-Visual-System based 3D video quality metric to be used in multiview encoding as an alternative to PSNR. Performance of our metric is tested in a 2-view case scenario. The quality of the compressed stereo pair, formed from a decoded view and a synthesized view, is evaluated at the encoder side. The performance is verified through a series of subjective tests and compared with that of PSNR, SSIM, MS-SSIM, VIFp, and VQM metrics. Experimental results showed that our 3D quality metric has the highest correlation with Mean Opinion Scores (MOS) compared to the other tested metrics

ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11 - JCT3V-C0032: A human visual system based 3D video quality metric

This contribution proposes a full-reference Human-Visual-System based 3D video quality metric. In this report, the presented metric is used to evaluate the quality of compressed stereo pair formed from a decoded view and a synthesized view. The performance of the proposed metric is verified through a series of subjective tests and compared with that of PSNR, SSIM, MS-SSIM, VIFp, and VQM metrics. The experimental results show that HV3D has the highest correlation with Mean Opinion Scores (MOS) compared to other tested metrics.Comment: arXiv admin note: substantial text overlap with arXiv:1803.04624, arXiv:1803.0462

Effect of High Frame Rates on 3D Video Quality of Experience

In this paper, we study the effect of 3D videos with increased frame rates on the viewers quality of experience. We performed a series of subjective tests to seek the subjects preferences among videos of the same scene at four different frame rates: 24, 30, 48, and 60 frames per second (fps). Results revealed that subjects clearly prefer higher frame rates. In particular, Mean Opinion Score (MOS) values associated with the 60 fps 3D videos were 55% greater than MOS values of the 24 fps 3D videos

An Efficient Human Visual System Based Quality Metric for 3D Video

Stereoscopic video technologies have been introduced to the consumer market in the past few years. A key factor in designing a 3D system is to understand how different visual cues and distortions affect the perceptual quality of stereoscopic video. The ultimate way to assess 3D video quality is through subjective tests. However, subjective evaluation is time consuming, expensive, and in some cases not possible. The other solution is developing objective quality metrics, which attempt to model the Human Visual System (HVS) in order to assess perceptual quality. Although several 2D quality metrics have been proposed for still images and videos, in the case of 3D efforts are only at the initial stages. In this paper, we propose a new full-reference quality metric for 3D content. Our method mimics HVS by fusing information of both the left and right views to construct the cyclopean view, as well as taking to account the sensitivity of HVS to contrast and the disparity of the views. In addition, a temporal pooling strategy is utilized to address the effect of temporal variations of the quality in the video. Performance evaluations showed that our 3D quality metric quantifies quality degradation caused by several representative types of distortions very accurately, with Pearson correlation coefficient of 90.8 %, a competitive performance compared to the state-of-the-art 3D quality metrics

3D Video Quality Metric for Mobile Applications

In this paper, we propose a new full-reference quality metric for mobile 3D content. Our method is modeled around the Human Visual System, fusing the information of both left and right channels, considering color components, the cyclopean views of the two videos and disparity. Our method is assessing the quality of 3D videos displayed on a mobile 3DTV, taking into account the effect of resolution, distance from the viewers eyes, and dimensions of the mobile display. Performance evaluations showed that our mobile 3D quality metric monitors the degradation of quality caused by several representative types of distortion with 82 percent correlation with results of subjective tests, an accuracy much better than that of the state of the art mobile 3D quality metric.Comment: arXiv admin note: substantial text overlap with arXiv:1803.04624; text overlap with arXiv:1803.04832 and arXiv:1803.0483

A Learning-Based Visual Saliency Fusion Model for High Dynamic Range Video (LBVS-HDR)

Saliency prediction for Standard Dynamic Range (SDR) videos has been well explored in the last decade. However, limited studies are available on High Dynamic Range (HDR) Visual Attention Models (VAMs). Considering that the characteristic of HDR content in terms of dynamic range and color gamut is quite different than those of SDR content, it is essential to identify the importance of different saliency attributes of HDR videos for designing a VAM and understand how to combine these features. To this end we propose a learning-based visual saliency fusion method for HDR content (LVBS-HDR) to combine various visual saliency features. In our approach various conspicuity maps are extracted from HDR data, and then for fusing conspicuity maps, a Random Forests algorithm is used to train a model based on the collected data from an eye-tracking experiment. Performance evaluations demonstrate the superiority of the proposed fusion method against other existing fusion methods

Benchmark 3D eye-tracking dataset for visual saliency prediction on stereoscopic 3D video

Visual Attention Models (VAMs) predict the location of an image or video regions that are most likely to attract human attention. Although saliency detection is well explored for 2D image and video content, there are only few attempts made to design 3D saliency prediction models. Newly proposed 3D visual attention models have to be validated over large-scale video saliency prediction datasets, which also contain results of eye-tracking information. There are several publicly available eye-tracking datasets for 2D image and video content. In the case of 3D, however, there is still a need for large-scale video saliency datasets for the research community for validating different 3D-VAMs. In this paper, we introduce a large-scale dataset containing eye-tracking data collected from 61 stereoscopic 3D videos (and also 2D versions of those) and 24 subjects participated in a free-viewing test. We evaluate the performance of the existing saliency detection methods over the proposed dataset. In addition, we created an online benchmark for validating the performance of the existing 2D and 3D visual attention models and facilitate addition of new VAMs to the benchmark. Our benchmark currently contains 50 different VAMs