Kvazaar HEVC encoder for efficient intra coding

Viitanen, Marko; Koivula, Ari; Lemmetti, Ari; Vanne, Jarno; Hämäläinen, Timo D.

doi:10.1109/iscas.2015.7168970

Cited by 29 publications

(11 citation statements)

References 5 publications

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“…All experimentations are performed on one core of the EmETXe-i87M0 platform from Arbor Technologies based on an Intel Core i5-4402E processor at 1.6 GHz. The used Hevc software encoder is the real time Kvazaar [13,14,30] in All Intra (AI) configuration. Since the configuration aims to be real-time, from [16], the Rate-Distortion Optimisation Quantization (RDOQ) [10] and the Intra transform skipping [15] features are disabled.…”

Section: Methodsmentioning

confidence: 99%

“…non-real-time) Hevc test Model (HM) encoder and do not offer the possibility to tune compression quality and complexity. Moreover, a real-time encoder such as Kvazaar is up to 10 times faster than HM [30]. The performance of state-of-the-art solutions based on HM are biased because they are measured comparatively to a gigantic compression time.…”

Section: Introductionmentioning

confidence: 99%

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On predicting the HEVC intra quad-tree partitioning with tunable energy and rate-distortion

Mercat

Arrestier

Pelcat

et al. 2018

J Real-Time Image Proc

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Future evolutions of the Internet of Things (IoT) are likely to boost mobile video demand to an unprecedented level. A large number of battery-powered systems will then integrate an High Efficiency Video Coding (Hevc) codec, implementing the latest video encoding standard from MPEG, and these systems will need to be energy efficient. Constraining the energy consumption of Hevc encoders is a challenging task, especially for embedded applications based on software encoders. The most efficient approach to manage the energy consumption of an Hevc encoder consists in optimizing the quad-tree partitioning and balance compression efficiency and energy consumption. The quad-tree partitioning splits the image into encoding units of variable sizes. The optimal size for a unit is content dependent and affects the encoding efficiency. Finding this optimal repartition is complex and the energy required by the so-called Rate-Distortion Optimization (RDO) process dominates the encoder energy consumption. For the purpose of budgeting the energy consumption of a real-time Hevc encoder, we propose in this paper a variance-aware quad-tree prediction that limits the energetic cost of the RDO process. The predictor is moreover adjustable by two parameters, (∆, ∆), offering a tradeoff between energetic gains and compression efficiency. Experimental results show that the proposed energy reduction scheme is able to reduce the energy consumption of a real-time Hevc encoder by 45% to 62% for a bit rate increase of respectively 0.49% and 3.4%. Moreover, the flexibility offered by parameters (∆, ∆) opens new opportunities for energy-aware encoding management.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On predicting the HEVC intra quad-tree partitioning with tunable energy and rate-distortion

Mercat

Arrestier

Pelcat

et al. 2018

J Real-Time Image Proc

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“…As the original videos are not tile-based, they had to be re-encoded. After extracting the raw YUV files, making use of the Kvazaar encoder [30], the videos were re-encoded in a HEVC tile-based version, considering two tiling schemes: 8 × 4 and 12 × 4 [15,24]. In addition, each tiling scheme was encoded to three quality representations, namely 720p (1.8Mb/s), 1080p (2.7Mb/s) and 4K (6Mb/s).…”

Section: Vr Dataset Generationmentioning

confidence: 99%

Predicting the performance of virtual reality video streaming in mobile networks

Filho

Luizelli

Vega

et al. 2018

Proceedings of the 9th ACM Multimedia Systems Conference

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The demand of Virtual Reality (VR) video streaming to mobile devices is booming, as VR becomes accessible to the general public. However, the variability of conditions of mobile networks affects the perception of this type of high-bandwidth-demanding services in unexpected ways. In this situation, there is a need for novel performance assessment models fit to the new VR applications. In this paper, we present PERCEIVE, a two-stage method for predicting the perceived quality of adaptive VR videos when streamed through mobile networks. By means of machine learning techniques, our approach is able to first predict adaptive VR video playout performance, using network Quality of Service (QoS) indicators as predictors. In a second stage, it employs the predicted VR video playout performance metrics to model and estimate end-user perceived quality. The evaluation of PERCEIVE has been performed considering a real-world environment, in which VR videos are streamed while subjected to LTE/4G network condition. The accuracy of PERCEIVE has been assessed by means of the residual error between predicted and measured values. Our approach predicts the different performance metrics of the VR playout with an average prediction error lower than 3.7% and estimates the perceived quality with a prediction error lower than 4% for over 90% of all the tested cases. Moreover, it allows us to pinpoint the QoS conditions that affect adaptive VR streaming services the most.

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“…All experimentations are performed on one core of the EmETXe-i87M0 platform from Arbor Technologies based on an Intel Core i5-4402E processor at 1.6 GHz. The used HEVC software encoder is the real time Kvazaar [16,17,39] in All Intra (AI) configuration. Since the configuration aims to be real-time, from [20], the Rate-Distortion Optimisation Quantization (RDOQ) [14] and the Intra transform skipping [19] features are disabled.…”

Section: Experimental Set-up and Parametersmentioning

confidence: 99%

“…In this paper, we target energy reduction in a real-time context of an optimized software encoder. A real-time encoder such as Kvazaar is up to 10 times faster than HM [39]. The complexity reduction performance of state-of-the-art solutions based on HM are biased since they are measured with respect to a large compression time.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Approach Versus Machine Learning for One-Shot Quad-Tree Prediction in an Intra HEVC Encoder

Mercat

Arrestier

Pelcat

et al. 2018

J Sign Process Syst

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Evolutions of the Internet of Things (IoT) in the next years are likely to boost mobile video demand to an unprecedented level. A large number of battery-powered systems will integrate an HEVC video codec, implementing the latest encoding MPEG standard, and these systems will need to be energy efficient. Constraining the energy consumption of HEVC encoders is a challenging task, especially for embedded applications based on software encoders. The most efficient approach to reduce the energy consumption of an HEVC encoder consists in optimizing the quad-tree block partitioning of the image and trade-off compression efficiency and energy consumption by efficiently choosing the near-optimal pixel block sizes. For the purpose of reducing the energy consumption of a real-time HEVC Intra encoder, this paper proposes and compares two methods that predict the quad-tree partitioning in "one-shot", i.e. without iterating. These methods drastically limit the computational cost of the recursive Rate-Distortion Optimization (RDO) process. The first proposed method uses a Probabilistic approach whereas the second method is based on Machine Learning approach. Experimental results show that both methods are capable of reducing the energy consumption of an embedded HEVC encoder of 58% for a bit rate increase of respectively 3.93% and 3.6%.

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Kvazaar HEVC encoder for efficient intra coding

Cited by 29 publications

References 5 publications

On predicting the HEVC intra quad-tree partitioning with tunable energy and rate-distortion

On predicting the HEVC intra quad-tree partitioning with tunable energy and rate-distortion

Predicting the performance of virtual reality video streaming in mobile networks

Probabilistic Approach Versus Machine Learning for One-Shot Quad-Tree Prediction in an Intra HEVC Encoder

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