Low complexity depth intra coding combining fast intra mode and fast CU size decision in 3D-HEVC

Peng, Kuan-Kai; Chiang, Jui-Chiu; Lie, Wen-Nung

doi:10.1109/icip.2016.7532533

Cited by 22 publications

(24 citation statements)

References 5 publications

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“…Works in [1,9,15,25,40] use CTU texture complexities to predict the quad-tree partitioning. Min et al [1] propose to decide if a CU has to be split, non-split or if it is undetermined, using the global and local edge complexities in four different directions (horizontal, vertical, 45 • and 135 • diagonals) of CU and sub-CUs.…”

Section: Complexity Reduction Of the Quad-tree Partitioningmentioning

confidence: 99%

“…-CU var [7,15,21,25,29,30] Figure 9 Diagram of the Probabilistic proposed energy reduction scheme. The Learning Frames (F L ) are encoded with a full RDO process (unconstrained) and the block variances of the resulting quad-tree decomposition are used to compute the set of thresholds υ th (d).…”

Section: Decision Trees-based Partitioning Decisionsmentioning

confidence: 99%

“…As examples of related works, authors in [31] and [4] propose to use the correlation between the minimum depth of the co-located CTUs in the current and previous frames to skip computing some depth levels during the RDO process. Authors in [1,9,15,25,40] use CTU texture complexities to predict the quad-tree partitioning. All these solutions are based on reducing the complexity of an offline (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The chosen training sequence set is circled. Upper-CU var[15,21,25,29,30]: the variances of the upper CU luminance samples of depth d−1 (1 features). -Nhbr-CU var [7, 15]: the variances of the neighbouring CU luminance samples of the depth d in the Z-scan order (3 features).…”

mentioning

confidence: 99%

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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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Section: Complexity Reduction Of the Quad-tree Partitioningmentioning

confidence: 99%

Section: Decision Trees-based Partitioning Decisionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

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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show abstract

“…Authors in [23] and [4] propose to use the correlation between the minimum depth of the co-located CTUs in the current and previous frames to skip computing some depth levels during the RDO process. Authors in [1,7,31,11,19] use CTU texture complexities to predict the quad-tree partitioning. However, these solutions are based on reducing the complexity of the best effort (i.e.…”

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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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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Fast Depth Intra Mode Decision Based on DCT in 3D-HEVC

Yang

Dai

Zhang

et al. 2018

Pattern Recognition and Computer Vision

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Low complexity depth intra coding combining fast intra mode and fast CU size decision in 3D-HEVC

Cited by 22 publications

References 5 publications

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

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

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

Fast Depth Intra Mode Decision Based on DCT in 3D-HEVC

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