1,132 research outputs found
Objective and Subjective Evaluation of Spatially Transcoded Videos for Mobile Receivers
Digital television produces video signals with different bit rates, encoding formats, and spatial resolutions. To deliver video to users with different receivers, the content needs to be dynamically adapted. Transcoding devices convert video from one format into another. The reception of digital videos using mobile receivers, implies that the spatial resolution of the video must be adjusted to fit the small display. This paper presents subjective and objective quality analysis of spatially transcoded videos. Transcoding algorithms that downsample the video frames using the moving average, median, mode, weighted average and sigma filters are considered
Audiovisual preservation strategies, data models and value-chains
This is a report on preservation strategies, models and value-chains for digital file-based audiovisual content. The report includes: (a)current and emerging value-chains and business-models for audiovisual preservation;(b) a comparison of preservation strategies for audiovisual content including their strengths and weaknesses, and(c) a review of current preservation metadata models, and requirements for extension to support audiovisual files
ARCHANGEL: Tamper-proofing Video Archives using Temporal Content Hashes on the Blockchain
We present ARCHANGEL; a novel distributed ledger based system for assuring
the long-term integrity of digital video archives. First, we describe a novel
deep network architecture for computing compact temporal content hashes (TCHs)
from audio-visual streams with durations of minutes or hours. Our TCHs are
sensitive to accidental or malicious content modification (tampering) but
invariant to the codec used to encode the video. This is necessary due to the
curatorial requirement for archives to format shift video over time to ensure
future accessibility. Second, we describe how the TCHs (and the models used to
derive them) are secured via a proof-of-authority blockchain distributed across
multiple independent archives. We report on the efficacy of ARCHANGEL within
the context of a trial deployment in which the national government archives of
the United Kingdom, Estonia and Norway participated.Comment: Accepted to CVPR Blockchain Workshop 201
vSkyConf: Cloud-assisted Multi-party Mobile Video Conferencing
As an important application in the busy world today, mobile video
conferencing facilitates virtual face-to-face communication with friends,
families and colleagues, via their mobile devices on the move. However, how to
provision high-quality, multi-party video conferencing experiences over mobile
devices is still an open challenge. The fundamental reason behind is the lack
of computation and communication capacities on the mobile devices, to scale to
large conferencing sessions. In this paper, we present vSkyConf, a
cloud-assisted mobile video conferencing system to fundamentally improve the
quality and scale of multi-party mobile video conferencing. By novelly
employing a surrogate virtual machine in the cloud for each mobile user, we
allow fully scalable communication among the conference participants via their
surrogates, rather than directly. The surrogates exchange conferencing streams
among each other, transcode the streams to the most appropriate bit rates, and
buffer the streams for the most efficient delivery to the mobile recipients. A
fully decentralized, optimal algorithm is designed to decide the best paths of
streams and the most suitable surrogates for video transcoding along the paths,
such that the limited bandwidth is fully utilized to deliver streams of the
highest possible quality to the mobile recipients. We also carefully tailor a
buffering mechanism on each surrogate to cooperate with optimal stream
distribution. We have implemented vSkyConf based on Amazon EC2 and verified the
excellent performance of our design, as compared to the widely adopted unicast
solutions.Comment: 10 page
Algorithms and methods for video transcoding.
Video transcoding is the process of dynamic video adaptation. Dynamic video adaptation can be defined as the process of converting video from one format to another, changing the bit rate, frame rate or resolution of the encoded video, which is mainly necessitated by the end user requirements. H.264 has been the predominantly used video compression standard for the last 15 years. HEVC (High Efficiency Video Coding) is the latest video compression standard finalised in 2013, which is an improvement over H.264 video compression standard. HEVC performs significantly better than H.264 in terms of the Rate-Distortion performance. As H.264 has been widely used in the last decade, a large amount of video content exists in H.264 format. There is a need to convert H.264 video content to HEVC format to achieve better Rate-Distortion performance and to support legacy video formats on newer devices. However, the computational complexity of HEVC encoder is 2-10 times higher than that of H.264 encoder. This makes it necessary to develop low complexity video transcoding algorithms to transcode from H.264 to HEVC format. This research work proposes low complexity algorithms for H.264 to HEVC video transcoding. The proposed algorithms reduce the computational complexity of H.264 to HEVC video transcoding significantly, with negligible loss in Rate-Distortion performance. This work proposes three different video transcoding algorithms. The MV-based mode merge algorithm uses the block mode and MV variances to estimate the split/non-split decision as part of the HEVC block prediction process. The conditional probability-based mode mapping algorithm models HEVC blocks of sizes 16×16 and lower as a function of H.264 block modes, H.264 and HEVC Quantisation Parameters (QP). The motion-compensated MB residual-based mode mapping algorithm makes the split/non-split decision based on content-adaptive classification models. With a combination of the proposed set of algorithms, the computational complexity of the HEVC encoder is reduced by around 60%, with negligible loss in Rate-Distortion performance, outperforming existing state-of-art algorithms by 20-25% in terms of computational complexity. The proposed algorithms can be used in computation-constrained video transcoding applications, to support video format conversion in smart devices, migration of large-scale H.264 video content from host servers to HEVC, cloud computing-based transcoding applications, and also to support high quality videos over bandwidth-constrained networks
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