404 research outputs found
Decoding-complexity-aware HEVC encoding using a complexity–rate–distortion model
The energy consumption of Consumer Electronic (CE) devices during media playback is inexorably linked to the computational complexity of decoding compressed video. Reducing a CE device's the energy consumption is therefore becoming ever more challenging with the increasing video resolutions and the complexity of the video coding algorithms. To this end, this paper proposes a framework that alters the video bit stream to reduce the decoding complexity and simultaneously limits the impact on the coding efficiency. In this context, this paper (i) first performs an analysis to determine the trade-off between the decoding complexity, video quality and bit rate with respect to a reference decoder implementation on a General Purpose Processor (GPP) architecture. Thereafter, (ii) a novel generic decoding complexity-aware video coding algorithm is proposed to generate decoding complexity-rate-distortion optimized High Efficiency Video Coding (HEVC) bit streams.
The experimental results reveal that the bit streams generated by the proposed algorithm achieve 29.43% and 13.22% decoding complexity reductions for a similar video quality with minimal coding efficiency impact compared to the state-of-the-art approaches when applied to the HM16.0 and openHEVC decoder implementations, respectively. In addition, analysis of the energy consumption behavior for the same scenarios reveal up to 20% energy consumption reductions while achieving a similar video quality to that of HM 16.0 encoded HEVC bit streams
A Decoding-Complexity and Rate-Controlled Video-Coding Algorithm for HEVC
Video playback on mobile consumer electronic (CE) devices is plagued by fluctuations in the network bandwidth and by limitations in processing and energy availability at the individual devices. Seen as a potential solution, the state-of-the-art adaptive streaming mechanisms address the first aspect, yet the efficient control of the decoding-complexity and the energy use when decoding the video remain unaddressed. The quality of experience (QoE) of the end-users’ experiences, however, depends on the capability to adapt the bit streams to both these constraints (i.e., network bandwidth and device’s energy availability). As a solution, this paper proposes an encoding framework that is capable of generating video bit streams with arbitrary bit rates and decoding-complexity levels using a decoding-complexity–rate–distortion model. The proposed algorithm allocates rate and decoding-complexity levels across frames and coding tree units (CTUs) and adaptively derives the CTU-level coding parameters to achieve their imposed targets with minimal distortion. The experimental results reveal that the proposed algorithm can achieve the target bit rate and the decoding-complexity with 0.4% and 1.78% average errors, respectively, for multiple bit rate and decoding-complexity levels. The proposed algorithm also demonstrates a stable frame-wise rate and decoding-complexity control capability when achieving a decoding-complexity reduction of 10.11 (%/dB). The resultant decoding-complexity reduction translates into an overall energy-consumption reduction of up to 10.52 (%/dB) for a 1 dB peak signal-to-noise ratio (PSNR) quality loss compared to the HM 16.0 encoded bit streams
C3: High-performance and low-complexity neural compression from a single image or video
Most neural compression models are trained on large datasets of images or
videos in order to generalize to unseen data. Such generalization typically
requires large and expressive architectures with a high decoding complexity.
Here we introduce C3, a neural compression method with strong rate-distortion
(RD) performance that instead overfits a small model to each image or video
separately. The resulting decoding complexity of C3 can be an order of
magnitude lower than neural baselines with similar RD performance. C3 builds on
COOL-CHIC (Ladune et al.) and makes several simple and effective improvements
for images. We further develop new methodology to apply C3 to videos. On the
CLIC2020 image benchmark, we match the RD performance of VTM, the reference
implementation of the H.266 codec, with less than 3k MACs/pixel for decoding.
On the UVG video benchmark, we match the RD performance of the Video
Compression Transformer (Mentzer et al.), a well-established neural video
codec, with less than 5k MACs/pixel for decoding
Error resilience and concealment techniques for high-efficiency video coding
This thesis investigates the problem of robust coding and error concealment in High Efficiency Video Coding (HEVC). After a review of the current state of the art, a simulation study about error robustness, revealed that the HEVC has weak protection against network losses with significant impact on video quality degradation. Based on this evidence, the first contribution of this work is a new method to reduce the temporal dependencies between motion vectors, by improving the decoded video quality without compromising the compression efficiency. The second contribution of this thesis is a two-stage approach for reducing the mismatch of temporal predictions in case of video streams received with errors or lost data. At the encoding stage, the reference pictures are dynamically distributed based on a constrained Lagrangian rate-distortion optimization to reduce the number of predictions from a single reference. At the streaming stage, a prioritization algorithm, based on spatial dependencies, selects a reduced set of motion vectors to be transmitted, as side information, to reduce mismatched motion predictions at the decoder. The problem of error concealment-aware video coding is also investigated to enhance the overall error robustness. A new approach based on scalable coding and optimally error concealment selection is proposed, where the optimal error concealment modes are found by simulating transmission losses, followed by a saliency-weighted optimisation. Moreover, recovery residual information is encoded using a rate-controlled enhancement layer. Both are transmitted to the decoder to be used in case of data loss. Finally, an adaptive error resilience scheme is proposed to dynamically predict the video stream that achieves the highest decoded quality for a particular loss case. A neural network selects among the various video streams, encoded with different levels of compression efficiency and error protection, based on information from the video signal, the coded stream and the transmission network. Overall, the new robust video coding methods investigated in this thesis yield consistent quality gains in comparison with other existing methods and also the ones implemented in the HEVC reference software. Furthermore, the trade-off between coding efficiency and error robustness is also better in the proposed methods
Optimized Data Representation for Interactive Multiview Navigation
In contrary to traditional media streaming services where a unique media
content is delivered to different users, interactive multiview navigation
applications enable users to choose their own viewpoints and freely navigate in
a 3-D scene. The interactivity brings new challenges in addition to the
classical rate-distortion trade-off, which considers only the compression
performance and viewing quality. On the one hand, interactivity necessitates
sufficient viewpoints for richer navigation; on the other hand, it requires to
provide low bandwidth and delay costs for smooth navigation during view
transitions. In this paper, we formally describe the novel trade-offs posed by
the navigation interactivity and classical rate-distortion criterion. Based on
an original formulation, we look for the optimal design of the data
representation by introducing novel rate and distortion models and practical
solving algorithms. Experiments show that the proposed data representation
method outperforms the baseline solution by providing lower resource
consumptions and higher visual quality in all navigation configurations, which
certainly confirms the potential of the proposed data representation in
practical interactive navigation systems
A Survey on Energy Consumption and Environmental Impact of Video Streaming
Climate change challenges require a notable decrease in worldwide greenhouse
gas (GHG) emissions across technology sectors. Digital technologies, especially
video streaming, accounting for most Internet traffic, make no exception. Video
streaming demand increases with remote working, multimedia communication
services (e.g., WhatsApp, Skype), video streaming content (e.g., YouTube,
Netflix), video resolution (4K/8K, 50 fps/60 fps), and multi-view video, making
energy consumption and environmental footprint critical. This survey
contributes to a better understanding of sustainable and efficient video
streaming technologies by providing insights into the state-of-the-art and
potential future directions for researchers, developers, and engineers, service
providers, hosting platforms, and consumers. We widen this survey's focus on
content provisioning and content consumption based on the observation that
continuously active network equipment underneath video streaming consumes
substantial energy independent of the transmitted data type. We propose a
taxonomy of factors that affect the energy consumption in video streaming, such
as encoding schemes, resource requirements, storage, content retrieval,
decoding, and display. We identify notable weaknesses in video streaming that
require further research for improved energy efficiency: (1) fixed bitrate
ladders in HTTP live streaming; (2) inefficient hardware utilization of
existing video players; (3) lack of comprehensive open energy measurement
dataset covering various device types and coding parameters for reproducible
research
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