108 research outputs found
Efficient HEVC-based video adaptation using transcoding
In a video transmission system, it is important to take into account the great diversity of the network/end-user constraints. On the one hand, video content is typically streamed over a network that is characterized by different bandwidth capacities. In many cases, the bandwidth is insufficient to transfer the video at its original quality. On the other hand, a single video is often played by multiple devices like PCs, laptops, and cell phones. Obviously, a single video would not satisfy their different constraints.
These diversities of the network and devices capacity lead to the need for video adaptation techniques, e.g., a reduction of the bit rate or spatial resolution. Video transcoding, which modifies a property of the video without the change of the coding format, has been well-known as an efficient adaptation solution. However, this approach comes along with a high computational complexity, resulting in huge energy consumption in the network and possibly network latency.
This presentation provides several optimization strategies for the transcoding process of HEVC (the latest High Efficiency Video Coding standard) video streams. First, the computational complexity of a bit rate transcoder (transrater) is reduced. We proposed several techniques to speed-up the encoder of a transrater, notably a machine-learning-based approach and a novel coding-mode evaluation strategy have been proposed. Moreover, the motion estimation process of the encoder has been optimized with the use of decision theory and the proposed fast search patterns. Second, the issues and challenges of a spatial transcoder have been solved by using machine-learning algorithms. Thanks to their great performance, the proposed techniques are expected to significantly help HEVC gain popularity in a wide range of modern multimedia applications
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
Filling the gaps in video transcoder deployment in the cloud
Cloud-based deployment of content production and broadcast workflows has
continued to disrupt the industry after the pandemic. The key tools required
for unlocking cloud workflows, e.g., transcoding, metadata parsing, and
streaming playback, are increasingly commoditized. However, as video traffic
continues to increase there is a need to consider tools which offer
opportunities for further bitrate/quality gains as well as those which
facilitate cloud deployment. In this paper we consider preprocessing,
rate/distortion optimisation and cloud cost prediction tools which are only
just emerging from the research community. These tools are posed as part of the
per-clip optimisation approach to transcoding which has been adopted by large
streaming media processing entities but has yet to be made more widely
available for the industry.Comment: Camera-ready version of BEIT Conference at NAB 202
Advanced heterogeneous video transcoding
PhDVideo transcoding is an essential tool to promote inter-operability
between different video communication systems. This thesis presents
two novel video transcoders, both operating on bitstreams of the cur-
rent H.264/AVC standard. The first transcoder converts H.264/AVC
bitstreams to a Wavelet Scalable Video Codec (W-SVC), while the second targets the emerging High Efficiency Video Coding (HEVC).
Scalable Video Coding (SVC) enables low complexity adaptation
of compressed video, providing an efficient solution for content delivery
through heterogeneous networks. The transcoder proposed here aims at
exploiting the advantages offered by SVC technology when dealing with
conventional coders and legacy video, efficiently reusing information
found in the H.264/AVC bitstream to achieve a high rate-distortion
performance at a low complexity cost. Its main features include new
mode mapping algorithms that exploit the W-SVC larger macroblock
sizes, and a new state-of-the-art motion vector composition algorithm
that is able to tackle different coding configurations in the H.264/AVC
bitstream, including IPP or IBBP with multiple reference frames.
The emerging video coding standard, HEVC, is currently approaching the final stage of development prior to standardization. This thesis
proposes and evaluates several transcoding algorithms for the HEVC
codec. In particular, a transcoder based on a new method that is capable of complexity scalability, trading off rate-distortion performance
for complexity reduction, is proposed. Furthermore, other transcoding solutions are explored, based on a novel content-based modeling
approach, in which the transcoder adapts its parameters based on the
contents of the sequence being encoded.
Finally, the application of this research is not constrained to these
transcoders, as many of the techniques developed aim to contribute
to advance the research on this field, and have the potential to be
incorporated in different video transcoding architectures
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