The share of online video traffic in global carbon dioxide emissions is growing steadily. To comply with the demand for video media, dedicated compression techniques are continuously optimized, but at the expense of increasingly higher computational demands and thus rising energy consumption at the video encoder side. In order to find the best trade-off between compression and energy consumption, modeling encoding energy for a wide range of encoding parameters is crucial. We propose an encoding time and energy model for SVT-AV1 based on empirical relations between the encoding time and video parameters as well as encoder configurations. Furthermore, we model the influence of video content by established content descriptors such as spatial and temporal information. We then use the predicted encoding time to estimate the required energy demand and achieve a prediction error of 19.6% for encoding time and 20.9% for encoding energy.

Current developments in video encoding technology lead to continuously improving compression performance but at the expense of increasingly higher computational demands. Regarding the online video traffic increases during the last years and the concomitant need for video encoding, encoder complexity control mechanisms are required to restrict the processing time to a sufficient extent in order to find a reasonable trade-off between performance and complexity. We present a complexity control mechanism in SVT-AV1 by using speed-adaptive preset switching to comply with the remaining time budget. This method enables encoding with a user-defined time constraint within the complete preset range with an average precision of 8.9 % without introducing any additional latencies.

In recent years, screen content video is becoming increasingly popular in several major video applications, such as video recording and video conferencing. Due to the unique features of screen content videos that are not captured by camera sensors but produced artificially, dedicated coding tools have been developed for achieving significant compression efficiency gain. In recognition of the popularity of screen content applications, an open video dataset for screen content is proposed in this paper for the development of screen content coding technologies. The proposed video dataset consists of 12 typical screen content type video clips that are publicly available. In addition, to better understand the characteristics of the proposed video dataset, several major screen content coding tools in AOMedia Video 1 (AV1) have been evaluated on this dataset and analyzed in this paper.

This paper describes the algorithm and processing flow of the in-loop deblocking filter in the VP9 coding standard, one of the most computationally intensive toolsets of the VP9 codec. Due to its inherent data dependency, it is a great challenge to efficiently implement the algorithm on massively parallel computing architectures, such as a General Purpose Graphical Processing Unit (GPGPU). In this paper, we describe the challenges involved in a GPGPU implementation of the VP9 Deblocking filter and introduce an innovative thread dispatching approach to address the parallelization challenges. This approach has been successfully implemented and productized in the VP9 decoder and encoder solutions on Intel GPUs. In order to further improve the parallelism of the deblocking algorithm itself, an improved in-loop deblocking algorithm and process flow is jointly proposed by Intel and Microsoft for the upcoming AV1 codec standard, developed by the Alliance for Open Media (AOM). A description of the algorithm and evaluation of the quality impact of this algorithm is presented with respect to the current state of the art AV1 reference codec.

In wireless sensor networks, one of the main design challenges is to save severely constrained energy resources and obtain a long system lifetime. Low cost of sensors enables us to initially deploy a large number of sensor nodes in a very high density. Thus, a potential approach to solve a lifetime problem arises. That is to schedule sensor nodes work alternatively by configuring some of them an off-duty status that has lower energy consumption than the normal on-duty one. In a single wireless sensor network, sensors assume two main functionalities: sensing and communication. Minimizing energy consumption in a highly dense wireless sensor network needs to maximize off-duty nodes in both domains. Most of the previous work addressed only one kind of redundancy: sensing or communication alone. Based on our previous conclusion: "the communication range is twice the sensing range" is the sufficient condition and the tight lower bound to ensure that complete coverage preservation implies connectivity among active nodes, if the original network topology (consisting of all the deployed nodes) is connected, we study a node status configuration strategy in a tightly integrated two-phase mode for those sensor networks where the communication range is more than twice the sensing range by comparing this strategy with the other two in the term of off-node number, the experimental results show that it leads to more off-duty nodes in double domains without incurring the reduction of off-duty nodes in a single domain.

In this work, we investigate the problem of bit rate adaptation transcoding for transmitting video over burst-error wireless channels. In particular, we consider a scenario consisting of packet-based transmission with an automatic repeat request (ARQ) error control scheme. We first analyze the buffering implications of inserting a video transcoder at the wireless access point with the variable bit rate channel as the target. We derive the conditions that both the source encoder and transcoder buffers have to satisfy for preventing the decoder buffer from underflowing. Furthermore, we propose a bit rate adaptation algorithm for VBR transcoders used in the wireless access point. Our experimental results demonstrate that visual quality can be improved by the proposed algorithm without violating the end-to-end delay requirements.

In order to transmit pre-encoded digital video over heterogeneous networks, it is necessary to employ transcoding techniques that convert pre-encoded video streams into streams having different bit rates and quality. The specified problem is referred to as rate shaping or rate adaptation. In this work, we propose a new rate control scheme for H.263+ based video transcoding. The proposed rate control scheme is comprised of Frame-Layer bit allocation and Macroblock-Layer rate control. At the frame layer, scene context statistics from the incoming video stream are utilized to detect scene changes and determine frame type. The bit budget is allocated to frames according to their energy and frame types. At the macroblock layer, a novel linear Rate-Quantization model is used for selecting quantization parameters for macroblocks. Implementation and experimental results show that the proposed algorithm can provide accurate bit allocation, and can effectively alleviate visual quality degradation after scene changes. This rate adaptation scheme can be used to provide flexible video bit rate adaptation for transmission of pre-encoded video over heterogeneous networks.

The rapid development of wireless technologies and computer-embedded devices make it possible for people to use portable devices accessing multimedia information and service. In order to bring multimedia information and service to the various client devices while retaining the ability to go mobile, multimedia information must be adapted, which is referred to as media transcoding technology. In this paper, some related issues of media transcoding are discussed. Media transcoding techniques are classified from different perspectives. Then a general framework for media transcoding is proposed and explored. Video transcoding is discussed as the major research focus. Some methods in terms of video rate control, spatial resolution reduction, and heterogeneous video transcoding are explored.