167 research outputs found
Advanced methods and deep learning for video and satellite data compression
L'abstract è presente nell'allegato / the abstract is in the attachmen
Low-Complexity and Hardware-Friendly H.265/HEVC Encoder for Vehicular Ad-Hoc Networks
Real-time video streaming over vehicular ad-hoc networks (VANETs) has been considered as a critical challenge for road safety applications. The purpose of this paper is to reduce the computation complexity of high efficiency video coding (HEVC) encoder for VANETs. Based on a novel spatiotemporal neighborhood set, firstly the coding tree unit depth decision algorithm is presented by controlling the depth search range. Secondly, a Bayesian classifier is used for the prediction unit decision for inter-prediction, and prior probability value is calculated by Gibbs Random Field model. Simulation results show that the overall algorithm can significantly reduce encoding time with a reasonably low loss in encoding efficiency. Compared to HEVC reference software HM16.0, the encoding time is reduced by up to 63.96%, while the Bjontegaard delta bit-rate is increased by only 0.76–0.80% on average. Moreover, the proposed HEVC encoder is low-complexity and hardware-friendly for video codecs that reside on mobile vehicles for VANETs
Towards Hybrid-Optimization Video Coding
Video coding is a mathematical optimization problem of rate and distortion
essentially. To solve this complex optimization problem, two popular video
coding frameworks have been developed: block-based hybrid video coding and
end-to-end learned video coding. If we rethink video coding from the
perspective of optimization, we find that the existing two frameworks represent
two directions of optimization solutions. Block-based hybrid coding represents
the discrete optimization solution because those irrelevant coding modes are
discrete in mathematics. It searches for the best one among multiple starting
points (i.e. modes). However, the search is not efficient enough. On the other
hand, end-to-end learned coding represents the continuous optimization solution
because the gradient descent is based on a continuous function. It optimizes a
group of model parameters efficiently by the numerical algorithm. However,
limited by only one starting point, it is easy to fall into the local optimum.
To better solve the optimization problem, we propose to regard video coding as
a hybrid of the discrete and continuous optimization problem, and use both
search and numerical algorithm to solve it. Our idea is to provide multiple
discrete starting points in the global space and optimize the local optimum
around each point by numerical algorithm efficiently. Finally, we search for
the global optimum among those local optimums. Guided by the hybrid
optimization idea, we design a hybrid optimization video coding framework,
which is built on continuous deep networks entirely and also contains some
discrete modes. We conduct a comprehensive set of experiments. Compared to the
continuous optimization framework, our method outperforms pure learned video
coding methods. Meanwhile, compared to the discrete optimization framework, our
method achieves comparable performance to HEVC reference software HM16.10 in
PSNR
3D coding tools final report
Livrable D4.3 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D4.3 du projet. Son titre : 3D coding tools final repor
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