Fast partitioning algorithm for HEVC Intra frame coding using machine learning

Ruiz, Damian; Adzic, Velibor; Fernández–Escribano, Gerardo; Kalva, Hari; Martínez, José Luis; Cuenca, Pedro

doi:10.1109/icip.2014.7025835

Cited by 35 publications

(15 citation statements)

References 14 publications

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“…Shen et al in [13] apply the same Bayesian theory to reduce the number of candidate TU sizes by exploiting the correlation between the variance of residual coefficients and the optimal transform size. In [14] a machine learning approach is proposed by using a set of decision trees for the CTU tree pruning that uses spatial first order attributes from the CU and sub-CUs.…”

Section: Related Workmentioning

confidence: 99%

“…Multiple approaches have been proposed in the literature for the reduction of the HEVC complexity, many of which focus on the reduction of the number of the block sizes to be evaluated [12][13][14][15][16][17][18][19] using a tree pruning scheme. Other approaches [20][21][22][23][24][25][26][27][28] are centered in the detection of the most probable intra direction, in order to avoid the evaluation of the full range of prediction modes by the RDO.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast intra mode decision algorithm based on texture orientation detection in HEVC

Ruiz

Fernández–Escribano

Martínez

et al. 2016

Signal Processing: Image Communication

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast intra mode decision algorithm based on texture orientation detection in HEVC

Ruiz

Fernández–Escribano

Martínez

et al. 2016

Signal Processing: Image Communication

Self Cite

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“…In addition to the widely adopted SVM and NN based approaches, several other machine learning techniques have been utilized to develop fast encoding algorithms for HEVC. For example, logistic regression [15], decision trees [16], [17], random forest [18], and Bayesian classification [19] are some of the state-of-the-art learning based approaches that have been considered in the literature for reducing HEVC's encoding complexity.…”

Section: Other Machine Learning Based Approachesmentioning

confidence: 99%

Machine Learning Approaches for Intra-Prediction in HEVC

Erabadda

Mallikarachchi

Kulupana

et al. 2018

2018 IEEE 7th Global Conference on Consumer Electronics (GCCE)

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The use of machine learning techniques for encoding complexity reduction in recent video coding standards such as High Efficiency Video Coding (HEVC) has received prominent attention in the recent past. Yet, the dynamically changing nature of the video contents makes it evermore challenging to use rigid traditional inference models for predicting the encoding decisions for a given content. In this context, this paper investigates the resulting implications on the coding efficiency and the encoding complexity, when using offline trained and online trained machine-learning models for coding unit size selection in the HEVC intra-prediction. The experimental results demonstrate that the ground truth encoding statistics of the content being encoded, is crucial to the efficient encoding decision prediction when using machine learning based prediction models.

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“…Several works have been proposed that use Machine Learning based optimization to reduce the complexity of the HEVC encoding process. Authors of [29,30] an Intra CU size classifier based on data-mining with an offline classifier training. The classifier is a three-node decision tree using mean and variance of CUs and sub-CUs as characteristics.…”

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%

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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Fast partitioning algorithm for HEVC Intra frame coding using machine learning

Cited by 35 publications

References 14 publications

Fast intra mode decision algorithm based on texture orientation detection in HEVC

Fast intra mode decision algorithm based on texture orientation detection in HEVC

Machine Learning Approaches for Intra-Prediction in HEVC

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

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