Pub Date : 2015-06-01DOI: 10.1109/ICME.2015.7177455
Ashok Anand, Mehrdad Reshadi, Bowei Du, Hariharan Kolam, S. Jaiswal, Aditya Akella
Mobile web usage has significantly increased in last few years. There has been a lot of emphasis on providing good web page performance for mobile devices. Client-side caching can play a significant role in providing good web page performance, but unfortunately, traditional browser caches lack in various aspects leading to sub-optimal performance. More specifically, web applications do not have control on caching, e.g., which resources to cache, how to cache, etc., leading to ineffective cache utilization. Recently, HTML5 has introduced number of persistent storage APIs, that can provide required control for web applications. We evaluate these HTML5 storage options on various devices, and find that they can also meet the performance criteria of caching; in fact, some of the HTML5 storage APIs, e.g., localStorage, can provide even better performance than browser cache. Based on these insights, we make a case for application-managed hierarchical client-side cache, called HCache, that leverages these storage options as backends. We propose a novel API that allows web application developers to intelligently control the caching behavior and the usage of these storage options transparently. Our experiments with a prototype show that HCache can improve web page performance by up to 60%.
{"title":"A case for application-managed cache for browser","authors":"Ashok Anand, Mehrdad Reshadi, Bowei Du, Hariharan Kolam, S. Jaiswal, Aditya Akella","doi":"10.1109/ICME.2015.7177455","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177455","url":null,"abstract":"Mobile web usage has significantly increased in last few years. There has been a lot of emphasis on providing good web page performance for mobile devices. Client-side caching can play a significant role in providing good web page performance, but unfortunately, traditional browser caches lack in various aspects leading to sub-optimal performance. More specifically, web applications do not have control on caching, e.g., which resources to cache, how to cache, etc., leading to ineffective cache utilization. Recently, HTML5 has introduced number of persistent storage APIs, that can provide required control for web applications. We evaluate these HTML5 storage options on various devices, and find that they can also meet the performance criteria of caching; in fact, some of the HTML5 storage APIs, e.g., localStorage, can provide even better performance than browser cache. Based on these insights, we make a case for application-managed hierarchical client-side cache, called HCache, that leverages these storage options as backends. We propose a novel API that allows web application developers to intelligently control the caching behavior and the usage of these storage options transparently. Our experiments with a prototype show that HCache can improve web page performance by up to 60%.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114254653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-01DOI: 10.1109/ICME.2015.7177444
A. Balasubramanian, Liangping Ma, Gregory Sternberg
This paper presents an Active Queue Management (AQM) algorithm for improving the Quality of Experience (QoE) of video telephony over packet switched networks. The algorithm exploits the characteristics of the video coding structure, and builds on the Controlled Delay (Codel) active queue management algorithm recently proposed by Nichols and Jacobson to address the prevalent `bufferbloat' problem in the current Internet. The proposed algorithm, Loss Concentration based controlled Delay (LC-Codel), maintains low queuing delay which is essential for video telephony, while using loss concentration to improve video QoE. Simulation results show significant gains in QoE with negligible impact on cross traffic.
{"title":"Loss concentration based Controlled Delay: An Active Queue Management algorithm for enhanced Quality of Experience for video telephony","authors":"A. Balasubramanian, Liangping Ma, Gregory Sternberg","doi":"10.1109/ICME.2015.7177444","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177444","url":null,"abstract":"This paper presents an Active Queue Management (AQM) algorithm for improving the Quality of Experience (QoE) of video telephony over packet switched networks. The algorithm exploits the characteristics of the video coding structure, and builds on the Controlled Delay (Codel) active queue management algorithm recently proposed by Nichols and Jacobson to address the prevalent `bufferbloat' problem in the current Internet. The proposed algorithm, Loss Concentration based controlled Delay (LC-Codel), maintains low queuing delay which is essential for video telephony, while using loss concentration to improve video QoE. Simulation results show significant gains in QoE with negligible impact on cross traffic.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115482838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-01DOI: 10.1109/ICME.2015.7177456
Szu-Yu Chou, Yi-Hsuan Yang, Yu-Ching Lin
Evaluation is important to assess the performance of a computer system in fulfilling a certain user need. In the context of recommendation, researchers usually evaluate the performance of a recommender system by holding out a random subset of observed ratings and calculating the accuracy of the system in reproducing such ratings. This evaluation strategy, however, does not consider the fact that in a real-world setting we are actually given the observed ratings of the past and have to predict for the future. There might be new songs, which create the cold-start problem, and the users' musical preference might change over time. Moreover, the user satisfaction of a recommender system may be related to factors other than accuracy. In light of these observations, we propose in this paper a novel evaluation framework that uses various time-based data splitting methods and evaluation metrics to assess the performance of recommender systems. Using millions of listening records collected from a commercial music streaming service, we compare the performance of collaborative filtering (CF) and content-based (CB) models with low-level audio features and semantic audio descriptors. Our evaluation shows that the CB model with semantic descriptors obtains a better trade-off among accuracy, novelty, diversity, freshness and popularity, and can nicely deal with the cold-start problems of new songs.
{"title":"Evaluating music recommendation in a real-world setting: On data splitting and evaluation metrics","authors":"Szu-Yu Chou, Yi-Hsuan Yang, Yu-Ching Lin","doi":"10.1109/ICME.2015.7177456","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177456","url":null,"abstract":"Evaluation is important to assess the performance of a computer system in fulfilling a certain user need. In the context of recommendation, researchers usually evaluate the performance of a recommender system by holding out a random subset of observed ratings and calculating the accuracy of the system in reproducing such ratings. This evaluation strategy, however, does not consider the fact that in a real-world setting we are actually given the observed ratings of the past and have to predict for the future. There might be new songs, which create the cold-start problem, and the users' musical preference might change over time. Moreover, the user satisfaction of a recommender system may be related to factors other than accuracy. In light of these observations, we propose in this paper a novel evaluation framework that uses various time-based data splitting methods and evaluation metrics to assess the performance of recommender systems. Using millions of listening records collected from a commercial music streaming service, we compare the performance of collaborative filtering (CF) and content-based (CB) models with low-level audio features and semantic audio descriptors. Our evaluation shows that the CB model with semantic descriptors obtains a better trade-off among accuracy, novelty, diversity, freshness and popularity, and can nicely deal with the cold-start problems of new songs.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115807549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-01DOI: 10.1109/ICME.2015.7177451
Chia-Po Wei, Y. Wang
Undersampled face recognition deals with the problem in which, for each subject to be recognized, only one or few images are available in the gallery (training) set. Thus, it is very difficult to handle large intra-class variations for face images. In this paper, we propose a one-pass dictionary learning algorithm to derive an auxiliary dictionary from external data, which consists of image variants of the subjects not of interest (not to be recognized). The proposed algorithm not only allows us to efficiently model intra-class variations such as illumination and expression changes, it also exhibits excellent abilities in recognizing corrupted images due to occlusion. In our experiments, we will show that our method would perform favorably against existing sparse representation or dictionary learning based approaches. Moreover, our computation time is remarkably less than that of recent dictionary learning based face recognition methods. Therefore, the effectiveness and efficiency of our proposed algorithm can be successfully verified.
{"title":"Undersampled face recognition with one-pass dictionary learning","authors":"Chia-Po Wei, Y. Wang","doi":"10.1109/ICME.2015.7177451","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177451","url":null,"abstract":"Undersampled face recognition deals with the problem in which, for each subject to be recognized, only one or few images are available in the gallery (training) set. Thus, it is very difficult to handle large intra-class variations for face images. In this paper, we propose a one-pass dictionary learning algorithm to derive an auxiliary dictionary from external data, which consists of image variants of the subjects not of interest (not to be recognized). The proposed algorithm not only allows us to efficiently model intra-class variations such as illumination and expression changes, it also exhibits excellent abilities in recognizing corrupted images due to occlusion. In our experiments, we will show that our method would perform favorably against existing sparse representation or dictionary learning based approaches. Moreover, our computation time is remarkably less than that of recent dictionary learning based face recognition methods. Therefore, the effectiveness and efficiency of our proposed algorithm can be successfully verified.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125448176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-01DOI: 10.1109/ICME.2015.7177442
Lifang Wu, Lijuan Wang, Shuang Liu, Qingyang Zheng, Y. Jing, Chang Wen Chen, Bo Yan
No single retargeting approach performs well on all images and all target sizes; therefore, hybrid algorithms are often considered as promising alternatives. However, most hybrid schemes are time consuming. In this paper, we propose a fast hybrid framework in which the Fast Content-Aware Image Distance (FCAID) is used to connect fast seam carving with neighboring probability constraints (FSc_Neip) and scaling. FCAID is used to measure the image distance between the resized image given by FSc_Neip and the original image. This fast technique is embedded within the FSc_Neip framework. Our hybrid scheme is locally applied in strip regions. This makes the retargeting scheme globally non-homogeneous. Experimental results demonstrate that our approach comprehensively outperforms other state-of-the-art techniques in terms of image quality and computational complexity.
{"title":"Image retargeting by combining fast seam carving with neighboring probability (FSc_Neip) and scaling","authors":"Lifang Wu, Lijuan Wang, Shuang Liu, Qingyang Zheng, Y. Jing, Chang Wen Chen, Bo Yan","doi":"10.1109/ICME.2015.7177442","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177442","url":null,"abstract":"No single retargeting approach performs well on all images and all target sizes; therefore, hybrid algorithms are often considered as promising alternatives. However, most hybrid schemes are time consuming. In this paper, we propose a fast hybrid framework in which the Fast Content-Aware Image Distance (FCAID) is used to connect fast seam carving with neighboring probability constraints (FSc_Neip) and scaling. FCAID is used to measure the image distance between the resized image given by FSc_Neip and the original image. This fast technique is embedded within the FSc_Neip framework. Our hybrid scheme is locally applied in strip regions. This makes the retargeting scheme globally non-homogeneous. Experimental results demonstrate that our approach comprehensively outperforms other state-of-the-art techniques in terms of image quality and computational complexity.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126993884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-01DOI: 10.1109/ICME.2015.7177402
Wei-Yi Chang, Y. Wang
We propose a learning-based algorithm for body shape estimation, which only requires 2D clothing images taken in multiple views as the input data. Compared with the use of 3D scanners or depth cameras, although our setting is more user friendly, it also makes the learning and estimation problems more challenging. In addition to utilizing ground truth body images for constructing human body models at each view of interest, our work uniquely associates the anthropometric measurements (e.g., body height or leg length) across different views. For performing body shape estimation using multi-view clothing images, the proposed algorithm solves an optimization task which recovers the body shape with image and measurement reconstruction guarantees. In the experiments, we will show that the use of our proposed method would achieve satisfactory estimation results, and performs favorably against single-view or other baseline approaches for both body shape and measurement estimation.
{"title":"Seeing through the appearance: Body shape estimation using multi-view clothing images","authors":"Wei-Yi Chang, Y. Wang","doi":"10.1109/ICME.2015.7177402","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177402","url":null,"abstract":"We propose a learning-based algorithm for body shape estimation, which only requires 2D clothing images taken in multiple views as the input data. Compared with the use of 3D scanners or depth cameras, although our setting is more user friendly, it also makes the learning and estimation problems more challenging. In addition to utilizing ground truth body images for constructing human body models at each view of interest, our work uniquely associates the anthropometric measurements (e.g., body height or leg length) across different views. For performing body shape estimation using multi-view clothing images, the proposed algorithm solves an optimization task which recovers the body shape with image and measurement reconstruction guarantees. In the experiments, we will show that the use of our proposed method would achieve satisfactory estimation results, and performs favorably against single-view or other baseline approaches for both body shape and measurement estimation.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128052232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-01DOI: 10.1109/ICME.2015.7177489
Ionut Mironica, Ionut Cosmin Duta, B. Ionescu, N. Sebe
In this paper we introduce a new video description framework that replaces traditional Bag-of-Words with a combination of Fisher Kernels (FK) and Vector of Locally Aggregated Descriptors (VLAD). The main contributions are: (i) a fast algorithm to densely extract global frame features, easier and faster to compute than spatio-temporal local features; (ii) replacing the traditional k-means based vocabulary with a Random Forest approach that allows significant speedup; (iii) use of a modified VLAD and FK representation to replace the classic Bag-of-Words and obtaining better performance. We show that our framework is highly general and is not dependent on a particular type of descriptor. It achieves state-of-the-art results in several classification scenarios.
{"title":"Beyond Bag-of-Words: Fast video classification with Fisher Kernel Vector of Locally Aggregated Descriptors","authors":"Ionut Mironica, Ionut Cosmin Duta, B. Ionescu, N. Sebe","doi":"10.1109/ICME.2015.7177489","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177489","url":null,"abstract":"In this paper we introduce a new video description framework that replaces traditional Bag-of-Words with a combination of Fisher Kernels (FK) and Vector of Locally Aggregated Descriptors (VLAD). The main contributions are: (i) a fast algorithm to densely extract global frame features, easier and faster to compute than spatio-temporal local features; (ii) replacing the traditional k-means based vocabulary with a Random Forest approach that allows significant speedup; (iii) use of a modified VLAD and FK representation to replace the classic Bag-of-Words and obtaining better performance. We show that our framework is highly general and is not dependent on a particular type of descriptor. It achieves state-of-the-art results in several classification scenarios.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128081739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-01DOI: 10.1109/ICME.2015.7177388
Jianshu Chao, E. Steinbach, Lexing Xie
In many mobile visual analysis scenarios, compressed images are transmitted over a communication network for analysis at a server. Often, the processing at the server includes some form of feature extraction and matching. Image compression has been shown to have an adverse effect on feature matching performance. To address this issue, we propose to signal the feature keypoints as side information to the server, and extract only the feature descriptors from the compressed images. To this end, we propose an approach to efficiently encode the locations, scales, and orientations of keypoints extracted from the original image. Furthermore, we propose a new approach for selecting relevant yet fragile keypoints as side information for the image, thus further reducing the data volume. We evaluate the performance of our approach using the Stanford mobile augmented reality dataset. Results show that the feature matching performance is significantly improved for images at low bitrate.
{"title":"Keypoint encoding and transmission for improved feature extraction from compressed images","authors":"Jianshu Chao, E. Steinbach, Lexing Xie","doi":"10.1109/ICME.2015.7177388","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177388","url":null,"abstract":"In many mobile visual analysis scenarios, compressed images are transmitted over a communication network for analysis at a server. Often, the processing at the server includes some form of feature extraction and matching. Image compression has been shown to have an adverse effect on feature matching performance. To address this issue, we propose to signal the feature keypoints as side information to the server, and extract only the feature descriptors from the compressed images. To this end, we propose an approach to efficiently encode the locations, scales, and orientations of keypoints extracted from the original image. Furthermore, we propose a new approach for selecting relevant yet fragile keypoints as side information for the image, thus further reducing the data volume. We evaluate the performance of our approach using the Stanford mobile augmented reality dataset. Results show that the feature matching performance is significantly improved for images at low bitrate.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124508467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-01DOI: 10.1109/ICME.2015.7177474
Lino Ferreira, L. Cruz, P. Assunção
Efficient computation of visual saliency regions has been a research problem in the recent past, but in the case of 3D content no definite solutions exist. This paper presents a computational method to determine saliency regions in 3D video, based on fusion of three feature maps containing perceptually relevant information from spatial, temporal and depth dimensions. The proposed method follows a bottom-up approach to predict the 3D regions where observers tend to hold their gaze for longer periods. Fusion of the feature maps is combined with a center-bias weighting function to determine 3D visual saliency map. For validation and performance evaluation, a publicly available database of 3D video sequences and corresponding fixation density maps was used as ground-truth. The experimental results show that the proposed method achieves better performance than other state-of-art models.
{"title":"A method to compute saliency regions in 3D video based on fusion of feature maps","authors":"Lino Ferreira, L. Cruz, P. Assunção","doi":"10.1109/ICME.2015.7177474","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177474","url":null,"abstract":"Efficient computation of visual saliency regions has been a research problem in the recent past, but in the case of 3D content no definite solutions exist. This paper presents a computational method to determine saliency regions in 3D video, based on fusion of three feature maps containing perceptually relevant information from spatial, temporal and depth dimensions. The proposed method follows a bottom-up approach to predict the 3D regions where observers tend to hold their gaze for longer periods. Fusion of the feature maps is combined with a center-bias weighting function to determine 3D visual saliency map. For validation and performance evaluation, a publicly available database of 3D video sequences and corresponding fixation density maps was used as ground-truth. The experimental results show that the proposed method achieves better performance than other state-of-art models.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123613192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-01DOI: 10.1109/ICME.2015.7177519
Philippe Hanhart, C. D. Nolfo, T. Ebrahimi
Multiview autostereoscopic displays are considered as the future of 3DTV. However, these displays suffer from a high level of crosstalk, which negatively impacts quality of experience (QoE). In this paper, we propose a system to improve 3D QoE on multiview autostereoscopic displays. First, the display is characterized in terms of luminance distribution. Then, the luminance profiles are modeled using a limited set of parameters. A Kinect sensor is used to determine the viewer position in front of the display. Finally, the proposed system performs an intelligent on the fly allocation of the output views to minimize the perceived crosstalk. The user preference between 2D and 3D modes and the proposed system is evaluated. Results show that picture quality is significantly improved when compared to the standard 3D mode, for a similar depth perception and visual comfort.
{"title":"Active crosstalk reduction system for multiview autostereoscopic displays","authors":"Philippe Hanhart, C. D. Nolfo, T. Ebrahimi","doi":"10.1109/ICME.2015.7177519","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177519","url":null,"abstract":"Multiview autostereoscopic displays are considered as the future of 3DTV. However, these displays suffer from a high level of crosstalk, which negatively impacts quality of experience (QoE). In this paper, we propose a system to improve 3D QoE on multiview autostereoscopic displays. First, the display is characterized in terms of luminance distribution. Then, the luminance profiles are modeled using a limited set of parameters. A Kinect sensor is used to determine the viewer position in front of the display. Finally, the proposed system performs an intelligent on the fly allocation of the output views to minimize the perceived crosstalk. The user preference between 2D and 3D modes and the proposed system is evaluated. Results show that picture quality is significantly improved when compared to the standard 3D mode, for a similar depth perception and visual comfort.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122109369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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