Katseenseurannan sovellukset mielenkiintoisen alueen HEVC-pakkaukselle

The increase in video streaming services and video resolutions has exploded the volume of Internet video traffic. New video coding standards, such as High Efficiency Video Coding (HEVC) have been developed to mitigate this inevitable video data explosion with better compression. The aim of video coding is to reduce the video size while maintaining the best possible perceived quality. Region of Interest (ROI) encoding particularly addresses this objective by focusing on the areas that humans would pay the most attention at and encode them with higher quality than the non-ROI areas. Methods for finding the ROI, and video encoding in general, take advantage of the Human Visual System (HVS). Computational HVS models can be used for the ROI detection but all current state-of-the-art models are designed for still images. Eye tracking data can be used for creating and verifying these models, including models suitable for video, which in turn calls for a reliable way to collect eye tracking data. Eye tracking glasses allow the widest range of possible scenarios out of all eye tracking equipment. Therefore, the glasses are used in this work to collect eye tracking data from 41 different videos. The main contribution of this work is to present a real-time system using eye tracking data to enhance the perceived quality of the video. The proposed system makes use of video recorded from the scene camera of the eye tracking glasses and Kvazaar open-source HEVC encoder for video compression. The system was shown to provide better subjective quality over the native rate control algorithm of Kvazaar. The obtained results were evaluated with Eye tracking Weighted PSNR (EWPSNR) that represents the HVS better than traditional PSNR. The system is shown to achieve up to 33% bit rate reduction for the same EWPSNR and on average 5-10% reduction depending on the parameter set. Additionally, the encoding time is improved by 8-20%

Towards Computational Efficiency of Next Generation Multimedia Systems

To address throughput demands of complex applications (like Multimedia), a next-generation system designer needs to co-design and co-optimize the hardware and software layers. Hardware/software knobs must be tuned in synergy to increase the throughput efficiency. This thesis provides such algorithmic and architectural solutions, while considering the new technology challenges (power-cap and memory aging). The goal is to maximize the throughput efficiency, under timing- and hardware-constraints

Adaptive Streaming: From Bitrate Maximization to Rate-Distortion Optimization

The fundamental conflict between the increasing consumer demand for better Quality-of-Experience (QoE) and the limited supply of network resources has become significant challenges to modern video delivery systems. State-of-the-art adaptive bitrate (ABR) streaming algorithms are dedicated to drain available bandwidth in hope to improve viewers' QoE, resulting in inefficient use of network resources. In this thesis, we develop an alternative design paradigm, namely rate-distortion optimized streaming (RDOS), to balance the contrast demands from video consumers and service providers. Distinct from the traditional bitrate maximization paradigm, RDOS must operate at any given point along the rate-distortion curve, as specified by a trade-off parameter. The new paradigm has found plausible explanations in information theory, economics, and visual perception. To instantiate the new philosophy, we decompose adaptive streaming algorithms into three mutually independent components, including throughput predictor, reward function, and bitrate selector. We provide a unified framework to understand the connections among all existing ABR algorithms. The new perspective also illustrates the fundamental limitations of each algorithm by going behind its underlying assumptions. Based on the insights, we propose novel improvements to each of the three functional components. To alleviate a series of unrealistic assumptions behind bitrate-based QoE models, we develop a theoretically-grounded objective QoE model. The new objective QoE model combines the information from subject-rated streaming videos and the prior knowledge about human visual system (HVS) in a principled way. By analyzing a corpus of psychophysical experiments, we show the QoE function estimation can be formulated as a projection onto convex sets problem. The proposed model presents strong generalization capability over a broad range of source contents, video encoders, and viewing conditions. Most importantly, the QoE model disentangles bitrate with quality, making it an ideal component in the RDOS framework. In contrast to the existing throughput estimators that approximate the marginal probability distribution over all connections, we optimize the throughput predictor conditioned on each client. Although there are lack of training data for each Internet Protocol connection, we can leverage the latest advances in meta learning to incorporate the knowledge embedded in similar tasks. With a deliberately designed objective function, the algorithm learns to identify similar structures among different network characteristics from millions of realistic throughput traces. During the test phase, the model can quickly adapt to connection-level network characteristics with only a small amount of training data from novel streaming video clients with a small number of gradient steps. The enormous space of streaming videos, constantly progressing encoding schemes, and great diversity of throughput characteristics make it extremely challenging for modern data-driven bitrate selectors that are trained with limited samples to generalize well. To this end, we propose a Bayesian bitrate selection algorithm by adaptively fusing an online, robust, and short-term optimal controller with an offline, susceptible, and long-term optimal planner. Depending on the reliability of the two controllers in certain system states, the algorithm dynamically prioritizes the one of the two decision rules to obtain the optimal decision. To faithfully evaluate the performance of RDOS, we construct a large-scale streaming video dataset -- the Waterloo Streaming Video database. It contains a wide variety of high quality source contents, encoders, encoding profiles, realistic throughput traces, and viewing devices. Extensive objective evaluation demonstrates the proposed algorithm can deliver identical QoE to state-of-the-art ABR algorithms at a much lower cost. The improvement is also supported by so-far the largest subjective video quality assessment experiment