Adaptive Streaming of HEVC Tiled Videos Using MPEG-DASH

Concolato, Cyril; Feuvre, Jean Le; Denoual, Franck; Maze, Frederic; Nassor, Éric; Ouedraogo, Nael; Taquet, Jonathan

doi:10.1109/tcsvt.2017.2688491

Cited by 50 publications

(29 citation statements)

References 16 publications

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“…Herein, the re-encoding procedure is explained step-by-step. After downloading the original VR-videos "Google Spotlight-HELP" 6 and "Freestyle Skiing" 7 , the raw videos must be first extracted using the following command of FFMPEG 8 :…”

Section: Tile-based Has Vr-video Re-encodingmentioning

confidence: 99%

“…HAS approach is focused on encoding the source content at multiple quality representations (bitrates), while each quality representation is time-segmented into small parts (i.e., segments). To further optimize bandwidth usage, recent research investigations have proposed HAS variants in which quality representations are not only segmented in time but also spatially split into smaller pieces (i.e., tiles) [6].…”

Section: Introductionmentioning

confidence: 99%

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Predicting the performance of virtual reality video streaming in mobile networks

Filho

Luizelli

Vega

et al. 2018

Proceedings of the 9th ACM Multimedia Systems Conference

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The demand of Virtual Reality (VR) video streaming to mobile devices is booming, as VR becomes accessible to the general public. However, the variability of conditions of mobile networks affects the perception of this type of high-bandwidth-demanding services in unexpected ways. In this situation, there is a need for novel performance assessment models fit to the new VR applications. In this paper, we present PERCEIVE, a two-stage method for predicting the perceived quality of adaptive VR videos when streamed through mobile networks. By means of machine learning techniques, our approach is able to first predict adaptive VR video playout performance, using network Quality of Service (QoS) indicators as predictors. In a second stage, it employs the predicted VR video playout performance metrics to model and estimate end-user perceived quality. The evaluation of PERCEIVE has been performed considering a real-world environment, in which VR videos are streamed while subjected to LTE/4G network condition. The accuracy of PERCEIVE has been assessed by means of the residual error between predicted and measured values. Our approach predicts the different performance metrics of the VR playout with an average prediction error lower than 3.7% and estimates the perceived quality with a prediction error lower than 4% for over 90% of all the tested cases. Moreover, it allows us to pinpoint the QoS conditions that affect adaptive VR streaming services the most.

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Section: Tile-based Has Vr-video Re-encodingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting the performance of virtual reality video streaming in mobile networks

Filho

Luizelli

Vega

et al. 2018

Proceedings of the 9th ACM Multimedia Systems Conference

View full text Add to dashboard Cite

show abstract

“…The general technical architecture of object detection system based on sliding window is indicated as Fig. 4 [ [16][17][18][19]. …”

Section: Object Detection Based On Object Modellingmentioning

confidence: 99%

Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video

Qin

Zhao

2018

Teh. vjesn.

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Abstract:In order to improve the accuracy and real-time performance of abnormal behaviour identification in massive video monitoring data, the authors design intelligent video technology based on convolutional neural network deep learning and apply it to the smart city on the basis of summarizing video development technology. First, the technical framework of intelligent video monitoring algorithm is divided into bottom (object detection), middle (object identification) and high (behaviour analysis) layers. The object detection based on background modelling is applied to routine real-time detection and early warning. The object detection based on object modelling is applied to after-event data query and retrieval. The related optical flow algorithms are used to achieve the identification and detection of abnormal behaviours. In order to improve the accuracy, effectiveness and intelligence of identification, the deep learning technology based on convolutional neural network is applied to enhance the learning and identification ability of learning machine and realize the real-time upgrade of intelligence video's "brain". This research has a good popularization value in the application field of intelligent video technology.

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“…Once the blurred image G (1) is computed using Section III-A, the function downSample (·, f (·)) downsamples image G (1) by rejecting even rows and even columns, which reduces the resolution of the resulting image, g (1) , in half. Both the Gaussian blur and function downSample(·) can then be applied to the resulting image g (1) in order to compute a second scale of the input image I. The sequence of images G = {g (0) , g (1) , .…”

Section: A Background On Gaussian Pyramidsmentioning

confidence: 99%

Heterogeneous Spatial Quality for Omnidirectional Video

Hristova

Corbillon²,

Simon³

et al. 2018

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)

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A video with heterogeneous spatial quality is a video where some regions of the frame have a different quality than other regions (for instance, a better quality could mean more pixels and less encoding distortion). Such a quality-variable encoding is a key enabler of Virtual Reality application, with 360-degree videos. So far, the main technique that has been proposed to prepare spatially heterogeneous quality is based on the concept of tiling. More recently, Facebook has implemented another approach: the offset projection where more emphasis is put on a specific direction of the frame. In this paper, we study quality-variable 360-degree videos with two main contributions. First, we provide the theoretical analysis of the offset projection and show the impact of the parameter settings on the video quality. Second, we propose another approach which consists in preparing the 360-degree video from a Gaussian pyramid of downscaled and blurred versions of the video. We perform an evaluation of tiling, offset and Gaussian-based approaches in representative scenarios of heterogeneous spatial quality in 360-degree videos and highlight the main trade-off to consider when implementing these approaches.

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Adaptive Streaming of HEVC Tiled Videos Using MPEG-DASH

Cited by 50 publications

References 16 publications

Predicting the performance of virtual reality video streaming in mobile networks

Predicting the performance of virtual reality video streaming in mobile networks

Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video

Heterogeneous Spatial Quality for Omnidirectional Video

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