Fast CU size decision based on temporal homogeneity detection

Fernández, D. G.; Barrio, Alberto A. Del; Botella, Guillermo; Garcı́a, Carlos

doi:10.1109/dcis.2016.7845379

Cited by 6 publications

(7 citation statements)

References 9 publications

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“…Fernández et al [74] apply motion estimation on input images and homogeneity analysis is done that is further used to give CU split decision. Input frames are analyzed before encoder starts encoding and the process is done on the GPU; thus it does not introduce any overhead for the CPU.…”

Section: Texture Complexitymentioning

confidence: 99%

CTU depth decision algorithms for HEVC: A survey

Çetinkaya

Amirpour

Ghanbari

et al. 2021

Signal Processing: Image Communication

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High-Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The Coding Tree Unit (CTU) is the main building block used in HEVC. In the HEVC standard, frames are divided into CTUs with the predetermined size of up to 64 × 64 pixels. Each CTU is then divided recursively into a number of equally sized square areas, known as Coding Units (CUs). Although this diversity of frame partitioning increases encoding efficiency, it also causes an increase in the time complexity due to the increased number of ways to find the optimal partitioning. To address this complexity, numerous algorithms have been proposed to eliminate unnecessary searches during partitioning CTUs by exploiting the correlation in the video. In this paper, existing CTU depth decision algorithms for HEVC are surveyed. These algorithms are categorized into two groups, namely statistics and machine learning approaches. Statistics approaches are further subdivided into neighboring and inherent approaches. Neighboring approaches exploit the similarity between adjacent CTUs to limit the depth range of the current CTU, while inherent approaches use only the available information within the current CTU. Machine learning approaches try to extract and exploit similarities implicitly. Traditional methods like support vector machines or random forests use manually selected features, while recently proposed deep learning methods extract features during training. Finally, this paper discusses extending these methods to more recent video coding formats such as Versatile Video Coding (VVC) and AOMedia Video 1 (AV1).

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Section: Texture Complexitymentioning

confidence: 99%

CTU depth decision algorithms for HEVC: A survey

Çetinkaya

Amirpour

Ghanbari

et al. 2021

Signal Processing: Image Communication

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“…Therefore, a large number of improved algorithms have been proposed. These algorithms include fast CU/PU size decision such as that in [11,12], fast intra prediction mode decision such as that in [13,14], and fast mode decision such as that in [15,16]. In [16], it exploits the depth information of neighboring CUs to make an early CU split decision or CU pruning decision, which can save 37.91% computational complexity on average as compared with the current HM (HEVC test model) with only a 0.66% increase.…”

Section: Related Workmentioning

confidence: 99%

“…Many candidate modes in RMD can be excluded in advance. Therefore, we can use the VMAD and HMAD calculated in the proposed CU mode decision algorithm to divide 33 angular predictions into horizontal (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) and vertical (18-34) modes according to the texture directionality (Fig. 12).…”

Section: Texture Direction-based Prediction Mode Decision Algorithmmentioning

confidence: 99%

Fast intra algorithm based on texture characteristics for 360 videos

Zhang

Dong

et al. 2019

J Image Video Proc.

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With the rapid progress of virtual reality technology, 360 videos have become increasingly popular. Given that the resolution of a 360 video is ultra-high (generally 4K to 8K), the encoding time for this type of video is considerably high. To reduce encoding complexity, this study proposed a fast intra algorithm that is based on image texture characteristics. On the one hand, the proposed algorithm determines whether to terminate the coding unit partition early on the basis of texture complexity. On the other hand, the proposed algorithm reduces the number of candidate modes in mode decision according to texture directivity. Experimental results showed that the proposed algorithm can obtain an average time reduction rate of 53% and a Bjontegaard delta rate increase of only 1.3%, which is acceptable for rate distortion performance.

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“…This system is more efficient but also more complex, providing the encoder the capacity to select the proper partition sizes [1] [2]. In our previous works, several optimized algorithms [4][5][6][7] were proposed to accelerate the encoding process. In [7] an enhanced CU size decision algorithm based on temporally and spatially homogeneous regions detection is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…HEVC, released in 2013, is of immense complexity and requires a great computational effort [2][3], which makes real time execution very difficult to achieve, especially for HD and UHDTV resolutions. As HEVC is tremendously complex, several approaches can be found in literature that have tried to reduce it [4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Information fusion based techniques for HEVC

et al. 2017

Self Cite

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Aiming at the conflict circumstances of multi-parameter H.265/HEVC encoder system, the present paper introduces the analysis of many optimizations' set in order to improve the trade-off between quality, performance and power consumption for different reliable and accurate applications. This method is based on the Pareto optimization and has been tested with different resolutions on real-time encoders.

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Fast CU size decision based on temporal homogeneity detection

Cited by 6 publications

References 9 publications

CTU depth decision algorithms for HEVC: A survey

CTU depth decision algorithms for HEVC: A survey

Fast intra algorithm based on texture characteristics for 360 videos

Information fusion based techniques for HEVC

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