Selective CU depth range decision algorithm for HEVC encoder

Kim, Dong‐Hyeok; Kim, Yonghwan; Park, Woo-Chool

doi:10.1109/isce.2014.6884344

Cited by 6 publications

(8 citation statements)

References 5 publications

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“…In this section, statistics based approaches are categorized into two groups based on the main feature used by the respective approach: (i) Neighboring CTUs use information that is available from spatially and temporally neighboring CTUs and (ii) inherent approaches only use information that Table 4 Threshold values and their corresponding depth ranges used in [20].…”

Section: Statistics Based Approachesmentioning

confidence: 99%

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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: Statistics Based Approachesmentioning

confidence: 99%

“…There are several methods in the literature that exploit only temporally or spatially closed CU information [20,21,22,23]. Kim et al [20] propose to determine a search range for the current CTU using a threshold value estimated from the spatially neighboring CTUs which is calculated as Eq.…”

Section: Core Hevc Specificationmentioning

confidence: 99%

CTU depth decision algorithms for HEVC: A survey

Çetinkaya

Amirpour

Ghanbari

et al. 2021

Signal Processing: Image Communication

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“…The CU quadtree structure for depth maps in 3D-HEVC is inherited from HEVC. Theoretically, the fast algorithms [50][51][52][53][54] for texture videos in HEVC can also be used for depth maps directly. In [50], the quadtree depth range is determined by the coded quadtree information from previous frames and neighbouring CUs.…”

Section: Fast Cu Size Decision Algorithmsmentioning

confidence: 99%

“…In [50], the quadtree depth range is determined by the coded quadtree information from previous frames and neighbouring CUs. The quadtree information of neighbouring CUs was also exploited in [51,52] to make an early CU split decision or CU pruning decision. Using the variance value of the input image, the method in [53] early determines the CU sizes for texture intra coding.…”

Section: Fast Cu Size Decision Algorithmsmentioning

confidence: 99%

Overview of current development in depth map coding of 3D video and its future

Chan

Chen

et al. 2020

IET signal process.

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3D videos have attracted attention from academia and industry after great success in the film industry. Multiview video plus depth (MVD) is the most popular 3D video format to provide vivid 3D feeling and has been adopted as an international 3D video coding standard, namely 3D extension of high efficiency video coding (HEVC). MVD includes a limited number of textures and depth maps to synthesise virtual views. In MVD, depth samples describe the distance between a camera and an actual object as a grey-level image. The characteristics of depth maps are quite different from texture images. Consequently, new coding tools are designed for depth maps in 3D-HEVC to improve the coding efficiency at the expense of high-computational complexity, which faces great challenges in coding systems. Depth map coding is also an important technique in immersive media to support three degrees of freedom 3DoF+/6DoF applications such as virtual reality/augmented reality. The study starts with an overview of what has been done over the last decade in 3D-HEVC, especially depth map coding, regarding theories, methodologies, current research and state-of-the-art fast approaches. Following this, a comprehensive comparison of the reviewed techniques is presented, and an outlook on its future trends is provided.

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“…The SMP and AMP mode decision methods are thus proposed. The work in [19] computes the CU depth range using thresholds estimated from neighboring CU information.…”

Section: Introductionmentioning

confidence: 99%