Early CU Depth Decision and Reference Picture Selection for Low Complexity MV-HEVC

Khan, Sehrish; Muhammad, Nazeer; Farwa, Shabieh; Saba, Tanzila; Mahmood, Zahid

doi:10.3390/sym11040454

“…For MV-HEVC and 3D-HEVC, there is one extra co-located CTU in the interview frame, and for 3D-HEVC, there is also one extra CTU in the depth frame [15]. Numerous approaches are proposed that specifically aim to exploit those extra neighboring CTUs in these HEVC extensions [40,41,42,43,44,45,47,48,49,50,19,51,52]. These methods also follow similar approaches as methods proposed for standard HEVC.…”

Section: Discussionmentioning

confidence: 99%

“…Khan et al [48] use the maximum depth level of interview co-located CTU and its eight spatially neighboring CTUs as a threshold to stop splitting of the current CTU for 3D-HEVC.…”

Section: Mv-hevc and 3d-hevcmentioning

confidence: 99%

CTU depth decision algorithms for HEVC: A survey

Çetinkaya

¹

,

Amirpour

²

,

Ghanbari

³

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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“…For MV-HEVC and 3D-HEVC, there is one extra co-located CTU in the interview frame, and for 3D-HEVC, there is also one extra CTU in the depth frame [15]. Numerous approaches are proposed that specifically aim to exploit those extra neighboring CTUs in these HEVC extensions [40,41,42,43,44,45,47,48,49,50,19,51,52]. These methods also follow similar approaches as methods proposed for standard HEVC.…”

Section: Discussionmentioning

confidence: 99%

CTU Depth Decision Algorithms for HEVC: A Survey

Cetinkaya,

Amirpour,

Ghanbari

et al. 2021

Preprint

0

View full text Add to dashboard Cite

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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“…), avoid static or moving obstacles and hazards in close proximity, and provide directions not only for navigation support but also for personalized guidance in that area. Moreover, mobile cloud-based applications [6], methodologies for optimal estimation of trajectories using GPS and other sensors accessible from a mobile device [7], and algorithms enabling efficient data coding for video streaming [8] can be considered for enhanced user experience in this context. Users should be able to easily interact with the system through speech proposed in [12] combined two devices, a smart glass and a smart pair of shoes, where various sensors were integrated with Raspberry Pi, and the data from both devices are processed to provide more efficient navigation solutions.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-Aware Visual Perception System for Outdoor Navigation of the Visually Challenged

Dimas

¹

,

Diamantis

²

,

Kalozoumis

³

et al. 2020

Sensors

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Every day, visually challenged people (VCP) face mobility restrictions and accessibility limitations. A short walk to a nearby destination, which for other individuals is taken for granted, becomes a challenge. To tackle this problem, we propose a novel visual perception system for outdoor navigation that can be evolved into an everyday visual aid for VCP. The proposed methodology is integrated in a wearable visual perception system (VPS). The proposed approach efficiently incorporates deep learning, object recognition models, along with an obstacle detection methodology based on human eye fixation prediction using Generative Adversarial Networks. An uncertainty-aware modeling of the obstacle risk assessment and spatial localization has been employed, following a fuzzy logic approach, for robust obstacle detection. The above combination can translate the position and the type of detected obstacles into descriptive linguistic expressions, allowing the users to easily understand their location in the environment and avoid them. The performance and capabilities of the proposed method are investigated in the context of safe navigation of VCP in outdoor environments of cultural interest through obstacle recognition and detection. Additionally, a comparison between the proposed system and relevant state-of-the-art systems for the safe navigation of VCP, focused on design and user-requirements satisfaction, is performed.

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Early CU Depth Decision and Reference Picture Selection for Low Complexity MV-HEVC

Cited by 16 publications

References 32 publications

CTU depth decision algorithms for HEVC: A survey

CTU depth decision algorithms for HEVC: A survey

CTU Depth Decision Algorithms for HEVC: A Survey

Uncertainty-Aware Visual Perception System for Outdoor Navigation of the Visually Challenged

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