In conventional HTTP-based adaptive streaming (HAS), a video source is encoded at multiple levels of constant bitrate representations, and a client makes its representation selections according to the measured network bandwidth. While greatly simplifying adaptation to the varying network conditions, this strategy is not the best for optimizing the video quality experienced by end users. Quality fluctuation can be reduced if the natural variability of video content is taken into consideration. In this work, we study the design of a client rate adaptation algorithm to yield consistent video quality. We assume that clients have visibility into incoming video within a finite horizon. We also take advantage of the client-side video buffer, by using it as a breathing room for not only network bandwidth variability, but also video bitrate variability. The challenge, however, lies in how to balance these two variabilities to yield consistent video quality without risking a buffer underrun. We propose an optimization solution that uses an online algorithm to adapt the video bitrate step-by-step, while applying dynamic programming at each step. We incorporate our solution into PANDA -- a practical rate adaptation algorithm designed for HAS deployment at scale.
: On the study of temporal pooling of video quality, a recent work @cite_3 have shown that the overall impression of a viewer towards a video is greatly influenced by the single most severe event while the duration is neglected, which corroborate our choice of the optimization objective. A more recent study @cite_19 dedicated to temporal pooling for HAS proposes a more complicated linear dynamic system model with the intent to capture the hysteresis effect in human visual response. Joseph and de Veciana @cite_6 uses the difference between mean quality and quality variability as the pooling metric.
@cite_19 @cite_6 @cite_3
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It is generally recognized that severe video distortions that are transient in space and or time have a large effect on overall perceived video quality. In order to understand this phenomena, we study the distribution of spatio-temporally local quality scores obtained from several video quality assessment (VQA) algorithms on videos suffering from compression and lossy transmission over communication channels. We propose a content adaptive spatial and temporal pooling strategy based on the observed distribution. Our method adaptively emphasizes “worst” scores along both the spatial and temporal dimensions of a video sequence and also considers the perceptual effect of large-area cohesive motion flow such as egomotion. We demonstrate the efficacy of the method by testing it using three different VQA algorithms on the LIVE Video Quality database and the EPFL-PoliMI video quality database. In this paper, we present an end-to-end approach to simultaneously learn spatio-temporal features and corresponding similarity metric for video-based person re-identification. Given the video sequence of a person, features from each frame that are extracted from all levels of a deep convolutional network can preserve a higher spatial resolution from which we can model finer motion patterns. These low-level visual percepts are leveraged into a variant of recurrent model to characterize the temporal variation between time-steps. Features from all time-steps are then summarized using temporal pooling to produce an overall feature representation for the complete sequence. The deep convolutional network, recurrent layer, and the temporal pooling are jointly trained to extract comparable hidden-unit representations from input pair of time series to compute their corresponding similarity value. The proposed framework combines time series modeling and metric learning to jointly learn relevant features and a good similarity measure between time sequences of person. Experiments demonstrate that our approach achieves the state-of-the-art performance for video-based person re-identification on iLIDS-VID and PRID 2011, the two primary public datasets for this purpose. We propose a function-based temporal pooling method that captures the latent structure of the video sequence data - e.g., how frame-level features evolve over time in a video. We show how the parameters of a function that has been fit to the video data can serve as a robust new video representation. As a specific example, we learn a pooling function via ranking machines. By learning to rank the frame-level features of a video in chronological order, we obtain a new representation that captures the video-wide temporal dynamics of a video, suitable for action recognition. Other than ranking functions, we explore different parametric models that could also explain the temporal changes in videos. The proposed functional pooling methods, and rank pooling in particular, is easy to interpret and implement, fast to compute and effective in recognizing a wide variety of actions. We evaluate our method on various benchmarks for generic action, fine-grained action and gesture recognition. Results show that rank pooling brings an absolute improvement of 7-10 average pooling baseline. At the same time, rank pooling is compatible with and complementary to several appearance and local motion based methods and features, such as improved trajectories and deep learning features. A continuous evaluation of the end user’s quality-of-experience (QoE) is essential for efficient video streaming. This is crucial for networks with constrained resources that offer time-varying channel quality to its users. In hypertext transfer protocol-based video streaming, the QoE is measured by quantifying the perceptual impact of distortions caused by rate adaptation or interruptions in playback due to rebuffering events. The resulting impact on the QoE due to these distortions has been studied individually in the literature. However, the QoE is determined by an interplay of these distortions, and therefore necessitates a combined study of them. To the best of our knowledge, there is no publicly available database that studies these distortions jointly on a continuous time basis. In this paper, our contributions are twofold. First, we present a database consisting of videos at full high definition and ultrahigh definition resolutions. We consider various levels of rate adaptation and rebuffering distortions together in these videos as experienced in a typical realistic setting. A subjective evaluation of these videos is conducted on a continuous time scale. Second, we present a QoE evaluation framework comprising a learning-based model during playback and an exponential model during rebuffering. Furthermore, we perform an objective evaluation of popular video quality assessment and continuous time QoE metrics over the constructed database. The objective evaluation study demonstrates that the performance of the proposed QoE model is superior to that of the objective metrics. The database is publicly available for download at http: www.iith.ac.in lfovia downloads.html .