Sample Adaptive Offset Parallelism in HEVC

Ryu, Eun-Kyung; Nam, Jung-Hak; Lee, Seon-Oh; Jo, Hyun-Ho; Sim, Donggyu

doi:10.1007/978-94-007-6738-6_137

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…If the derived chroma intra mode is congruent to the initial chroma intra mode, then the Intra angular mode (i.e., mode-34) is used for the chroma, otherwise, the derived one is used. [15,17,18] for parallel encoding/decoding of a single picture. The input frame can be divided into various tiles each of them comprises an integer number of an individually decodable Coding Tree Block (CTB).…”

Section: Intra Prediction Modesmentioning

confidence: 99%

Privacy Protection in Real Time HEVC Standard Using Chaotic System

et al. 2020

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Video protection and access control have gathered steam over recent years. However, the most common methods encrypt the whole video bit stream as unique data without taking into account the structure of the compressed video. These full encryption solutions are time and power consuming and, thus, are not aligned with the real-time applications. In this paper, we propose a Selective Encryption (SE) solution for Region of Interest (ROI) security based on the tile concept in High Efficiency Video Coding (HEVC) standards and selective encryption of all sensitive parts in videos. The SE solution depends on a chaos-based stream cipher that encrypts a set of HEVC syntax elements normatively, that is, the bit stream can be decoded with a standard HEVC decoder, and a secret key is only required for ROI decryption. The proposed ROI encryption solution relies on the independent tile concept in HEVC that splits the video frame into independent rectangular areas. Tiles are used to pull out the ROI from the background and only the tiles figuring the ROI are encrypted. In inter coding, the independence of tiles is guaranteed by limiting the motion vectors of non-ROI to use only the unencrypted tiles in the reference frames. Experimental results have shown that the encryption solution performs secure video encryption in a real time context, with a diminutive bit rate and complexity overheads.

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Section: Intra Prediction Modesmentioning

confidence: 99%

Privacy Protection in Real Time HEVC Standard Using Chaotic System

et al. 2020

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“…In-loop filter parallelization is required to maximize the overall performance of the HEVC decoder. In addition, several studies have focused on SAO parallelization [11], [18]. In this paper, we propose deblocking filter parallelization and an efficient method of applying DLP to the HEVC deblocking filter.…”

Section: Proposed Deblocking Filter Parallelization Based On Commentioning

confidence: 99%

Load Balancing Based on Transform Unit Partition Information for High Efficiency Video Coding Deblocking Filter

et al. 2017

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In this paper, we propose a parallelization method for a High Efficiency Video Coding (HEVC) deblocking filter with transform unit (TU) split information. HEVC employs a deblocking filter to boost perceptual quality and coding efficiency. The deblocking filter was designed for data-level parallelism. In this paper, we demonstrate a method of distributing equal workloads to all cores or threads by anticipating the deblocking filter complexity based on the coding unit depth and TU split information. We determined that the average time saving of our proposed deblocking filter parallelization method has a speed-up factor that is 2% better than that of the uniformly distributed parallel deblocking filter, and 6% better than that of coding tree unit row distribution parallelism. In addition, we determined that the speed-up factor of our proposed deblocking filter parallelization method, in terms of percentage run-time, is up to 3.1 compared to the run-time of the HEVC test model 12.0 deblocking filter with a sequential implementation.

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On the use of deep learning and parallelism techniques to significantly reduce the HEVC intra-coding time

et al. 2022

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It is well-known that each new video coding standard significantly increases in computational complexity with respect to previous standards, and this is particularly true for the HEVC and VVC video coding standards. The development of techniques for reducing the required complexity without affecting the rate/distortion (R/D) performance is therefore always a topic of intense research interest. In this paper, we propose a combination of two powerful techniques, deep learning and parallel computing, to significantly reduce the complexity of the HEVC encoding engine. Our experimental results show that a combination of deep learning to reduce the CTU partitioning complexity with parallel strategies based on frame partitioning is able to achieve speedups of up to 26$$\times$$ × when 16 threads are used. The R/D penalty in terms of the BD-BR metric depends on the video content, the compression rate and the number of OpenMP threads, and was consistently between 0.35 and 10% for the video sequence test set used in our experiments

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Sample Adaptive Offset Parallelism in HEVC

Cited by 4 publications

References 3 publications

Privacy Protection in Real Time HEVC Standard Using Chaotic System

Privacy Protection in Real Time HEVC Standard Using Chaotic System

Load Balancing Based on Transform Unit Partition Information for High Efficiency Video Coding Deblocking Filter

On the use of deep learning and parallelism techniques to significantly reduce the HEVC intra-coding time

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