arXiv subjects

Jong-Seok Lee

Publications and source records attributed to Jong-Seok Lee.

65 records · Page 4Linked to original sources

arXiv2018

Music Popularity: Metrics, Characteristics, and Audio-Based Prediction

Understanding music popularity is important not only for the artists who create and perform music but also for the music-related industry. It has not been studied well how music popularity can be defined, what its characteristics are, and whether it can be predicted, which are addressed in this paper. We first define eight popularity metrics to cover multiple aspects of popularity. Then, the analysis of each popularity metric is conducted with long-term real-world chart data to deeply understand the characteristics of music popularity in the real world. We also build classification models for predicting popularity metrics using acoustic data. In particular, we focus on evaluating features describing music complexity together with other conventional acoustic features including MPEG-7 and Mel-frequency cepstral coefficient (MFCC) features. The results show that, although room still exists for improvement, it is feasible to predict the popularity metrics of a song significantly better than random chance based on its audio signal, particularly using both the complexity and MFCC features.

cs.MM↗

arXiv2018

Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network

Recently, several deep learning-based image super-resolution methods have been developed by stacking massive numbers of layers. However, this leads too large model sizes and high computational complexities, thus some recursive parameter-sharing methods have been also proposed. Nevertheless, their designs do not properly utilize the potential of the recursive operation. In this paper, we propose a novel, lightweight, and efficient super-resolution method to maximize the usefulness of the recursive architecture, by introducing block state-based recursive network. By taking advantage of utilizing the block state, the recursive part of our model can easily track the status of the current image features. We show the benefits of the proposed method in terms of model size, speed, and efficiency. In addition, we show that our method outperforms the other state-of-the-art methods.

cs.CV↗

arXiv2018

Generative adversarial network-based image super-resolution using perceptual content losses

In this paper, we propose a deep generative adversarial network for super-resolution considering the trade-off between perception and distortion. Based on good performance of a recently developed model for super-resolution, i.e., deep residual network using enhanced upscale modules (EUSR), the proposed model is trained to improve perceptual performance with only slight increase of distortion. For this purpose, together with the conventional content loss, i.e., reconstruction loss such as L1 or L2, we consider additional losses in the training phase, which are the discrete cosine transform coefficients loss and differential content loss. These consider perceptual part in the content loss, i.e., consideration of proper high frequency components is helpful for the trade-off problem in super-resolution. The experimental results show that our proposed model has good performance for both perception and distortion, and is effective in perceptual super-resolution applications.

cs.CV↗

arXiv2018

On Evaluating Perceptual Quality of Online User-Generated Videos

This paper deals with the issue of the perceptual quality evaluation of user-generated videos shared online, which is an important step toward designing video-sharing services that maximize users' satisfaction in terms of quality. We first analyze viewers' quality perception patterns by applying graph analysis techniques to subjective rating data. We then examine the performance of existing state-of-the-art objective metrics for the quality estimation of user-generated videos. In addition, we investigate the feasibility of metadata accompanied with videos in online video-sharing services for quality estimation. Finally, various issues in the quality assessment of online user-generated videos are discussed, including difficulties and opportunities.

cs.MM↗

arXiv2018

Perceptual Experience Analysis for Tone-mapped HDR Videos based on EEG and Peripheral Physiological Signals

High dynamic range (HDR) imaging has been attracting much attention as a technology that can provide immersive experience. Its ultimate goal is to provide better quality of experience (QoE) via enhanced contrast. In this paper, we analyze perceptual experience of tone-mapped HDR videos both explicitly by conducting a subjective questionnaire assessment and implicitly by using EEG and peripheral physiological signals. From the results of the subjective assessment, it is revealed that tone-mapped HDR videos are more interesting and more natural, and give better quality than low dynamic range (LDR) videos. Physiological signals were recorded during watching tone-mapped HDR and LDR videos, and classification systems are constructed to explore perceptual difference captured by the physiological signals. Significant difference in the physiological signals is observed between tone-mapped HDR and LDR videos in the classification under both a subject-dependent and a subject-independent scenarios. Also, significant difference in the signals between high versus low perceived contrast and overall quality is detected via classification under the subject-dependent scenario. Moreover, it is shown that features extracted from the gamma frequency band are effective for classification.

cs.HC↗

arXiv2018

Evaluation of Preference of Multimedia Content using Deep Neural Networks for Electroencephalography

Evaluation of quality of experience (QoE) based on electroencephalography (EEG) has received great attention due to its capability of real-time QoE monitoring of users. However, it still suffers from rather low recognition accuracy. In this paper, we propose a novel method using deep neural networks toward improved modeling of EEG and thereby improved recognition accuracy. In particular, we aim to model spatio-temporal characteristics relevant for QoE analysis within learning models. The results demonstrate the effectiveness of the proposed method.

cs.HC↗

arXiv2018

Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information

Emotion recognition based on electroencephalography (EEG) has received attention as a way to implement human-centric services. However, there is still much room for improvement, particularly in terms of the recognition accuracy. In this paper, we propose a novel deep learning approach using convolutional neural networks (CNNs) for EEG-based emotion recognition. In particular, we employ brain connectivity features that have not been used with deep learning models in previous studies, which can account for synchronous activations of different brain regions. In addition, we develop a method to effectively capture asymmetric brain activity patterns that are important for emotion recognition. Experimental results confirm the effectiveness of our approach.

cs.HC↗

arXiv2018

EEG-based video identification using graph signal modeling and graph convolutional neural network

This paper proposes a novel graph signal-based deep learning method for electroencephalography (EEG) and its application to EEG-based video identification. We present new methods to effectively represent EEG data as signals on graphs, and learn them using graph convolutional neural networks. Experimental results for video identification using EEG responses obtained while watching videos show the effectiveness of the proposed approach in comparison to existing methods. Effective schemes for graph signal representation of EEG are also discussed.

eess.SP↗

arXiv2018

Implicit Analysis of Perceptual Multimedia Experience Based on Physiological Response: A Review

The exponential growth of popularity of multimedia has led to needs for user-centric adaptive applications that manage multimedia content more effectively. Implicit analysis, which examines users' perceptual experience of multimedia by monitoring physiological or behavioral cues, has potential to satisfy such demands. Particularly, physiological signals categorized into cerebral physiological signals (electroencephalography, functional magnetic resonance imaging, and functional near-infrared spectroscopy) and peripheral physiological signals (heart rate, respiration, skin temperature, etc.) have recently received attention along with notable development of wearable physiological sensors. In this paper, we review existing studies on physiological signal analysis exploring perceptual experience of multimedia. Furthermore, we discuss current trends and challenges.

cs.HC↗

arXiv2017

Impact of Three-Dimensional Video Scalability on Multi-View Activity Recognition using Deep Learning

Human activity recognition is one of the important research topics in computer vision and video understanding. It is often assumed that high quality video sequences are available for recognition. However, relaxing such a requirement and implementing robust recognition using videos having reduced data rates can achieve efficiency in storing and transmitting video data. Three-dimensional video scalability, which refers to the possibility of reducing spatial, temporal, and quality resolutions of videos, is an effective way for flexible representation and management of video data. In this paper, we investigate the impact of the video scalability on multi-view activity recognition. We employ both a spatiotemporal feature extraction-based method and a deep learning-based method using convolutional and recurrent neural networks. The recognition performance of the two methods is examined, along with in-depth analysis regarding how their performance vary with respect to various scalability combinations. In particular, we demonstrate that the deep learning-based method can achieve significantly improved robustness in comparison to the feature-based method. Furthermore, we investigate optimal scalability combinations with respect to bitrate in order to provide useful guidelines for an optimal operation policy in resource-constrained activity recognition systems.

cs.MM↗

arXiv2016

QoE-aware Scalable Video Transmission in MIMO~Systems

An important concept in wireless systems has been quality of experience (QoE)-aware video transmission. Such communications are considered not only connection-based communications but also content-aware communications, since the video quality is closely related to the content itself. It becomes necessary therefore for video communications to utilize a cross-layer design (also known as joint source and channel coding). To provide efficient methods of allocating network resources, the wireless network uses its cross-layer knowledge to perform unequal error protection (UEP) solutions. In this article, we summarize the latest video transmission technologies that are based on scalable video coding (SVC) over multiple-input multiple-output (MIMO) systems with cross-layer designs. To provide insight into video transmission in wireless networks, we investigate UEP solutions in the delivering of video over massive MIMO systems. Our results show that in terms of quality of experience (QoE), SVC layer prioritization, which was considered important in the prior work, is not always beneficial in massive MIMO systems; consideration must be given to the content characteristics.

cs.IT↗