A Low-Complexity Video Encoder for Equirectangular Projected 360 Video Content

Ray, Bappaditya; Jung, Joël; Larabi, Mohamed–Chaker

doi:10.1109/icassp.2018.8462368

Cited by 23 publications

(16 citation statements)

References 9 publications

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“…The average results for each algorithm are presented in Table 1, where InterTech is the result of employing all interframes prediction algorithms simultaneously (i.e., Early Skip, Reduced SR, and Reduced FME), and IntraTech is the combination of Reduced Intra Sizes and a previous work published at [11]. Table I also presents the average results of related works: Liu [12] and Ray [13] present proposals to accelerate the interframes prediction of ERP 360 videos, whereas Wang [14] and Zhang [15] propose to accelerate the intraframe prediction of ERP 360 videos. Liu [12] uses a KNN classifier to discard some unlikely block sizes during interframes prediction; Ray [13] discards unlikely block sizes during interframes prediction and reduces FME precision based on block position; Wang [14] discards some prediction modes and block sizes during intraframe prediction based on block position; and Zhang [15] evaluates the texture of the blocks to evaluate a subset of prediction modes and block sizes.…”

Section: Resultsmentioning

confidence: 99%

“…Table I also presents the average results of related works: Liu [12] and Ray [13] present proposals to accelerate the interframes prediction of ERP 360 videos, whereas Wang [14] and Zhang [15] propose to accelerate the intraframe prediction of ERP 360 videos. Liu [12] uses a KNN classifier to discard some unlikely block sizes during interframes prediction; Ray [13] discards unlikely block sizes during interframes prediction and reduces FME precision based on block position; Wang [14] discards some prediction modes and block sizes during intraframe prediction based on block position; and Zhang [15] evaluates the texture of the blocks to evaluate a subset of prediction modes and block sizes. It must be noted that Ray [13] uses the encoder Joint Exploration Model (JEM) [16], an exploratory encoder built on top of HEVC to evaluate new encoding tools for future video coding standards.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Exploring ERP Distortions to Reduce the Encoding Time of 360 Videos

Storch¹,

Zatt²,

Agostini³

et al. 2021

Anais Estendidos Do XXVII Simpósio Brasileiro De Sistemas Multimídia E Web (WebMedia 2021)

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The encoding of 360 videos presents many challenges. These spherical videos require a planar projection stage prior to encoding, and there are no studies on how projection interferes with the encoder decisions. Also, due to the increased resolution, encoding 360 videos is computationally costlier than conventional videos. In this work, we perform an extensive evaluation of how the texture distortions caused by the projection interfere with the encoder’s behavior and exploit these interferences to propose multiple fast algorithms to accelerate the encoding. Experimental results show that the proposed algorithms reduce encoding time significantly with minor coding efficiency penalties, while the evaluations expose novel insights into the encoder behavior. The reached results are published in important events and journals.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Exploring ERP Distortions to Reduce the Encoding Time of 360 Videos

Storch¹,

Zatt²,

Agostini³

et al. 2021

Anais Estendidos Do XXVII Simpósio Brasileiro De Sistemas Multimídia E Web (WebMedia 2021)

View full text Add to dashboard Cite

show abstract

“…For instance, several research works have been investigated on fast approaches for 360-degree video coding [98][99][100][101][102][103][104] but without any depth information, i.e. 3DoF.…”

Section: Role Of Depth Coding In Future Immersive Mediamentioning

confidence: 99%

“…The algorithms proposed in [98][99][100] have fast intra prediction, while the works in [101,102,103] suggested having fast CU approaches and a fast PU approach, respectively. Besides, the algorithm in [104] developed adaptive motion vector (MV) resolution to achieve a low-complexity encoder. On the other hand, some research works focus on adaptive encoding or streaming for VR according to the viewer's viewport [105][106][107].…”

Section: Role Of Depth Coding In Future Immersive Mediamentioning

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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“…К первой группе относятся методы, в которых панорама проецируется на плоскость с помощью одной или нескольких картографических проекций. Широко распространены решения, основанные на эквидистантной цилиндрической проекции [7] (в настоящее время в данной проекции видео 360 загружается на YouTube). Эквидистантная проекция отображает панораму в истинном масштабе только вдоль экватора, а при приближении к полюсам масштаб увеличивается.…”

Section: Introductionunclassified

Efficient methods and algorithms to synthesize 360-degree video based on cubemap projection of virtual environment

Тимохин¹,

Михайлюк²,

Вожегов³

2020

Proceedings of ISP RAS

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Аннотация. В статье рассматривается задача создания и воспроизведения панорамного видео с обзором 360 градусов, обеспечивающего погружение исследователя в виртуальную среду вне родительской системы виртуального окружения (СВО). Для решения этой задачи предлагается расширение метода проекции в кубическую карту, при котором разрешение карты определяется с учетом угла обзора камеры зрителя и разрешения экрана (Adequate Cubemap Projection, ACMP). В работе исследовано влияние ориентации камеры зрителя внутри куба на отношение «пиксел карты/пиксел экрана», определяющее качество визуализации панорамы, и предложен метод вычисления разрешения кубической карты для качественной визуализации панорамы при всех возможных ориентациях камеры. В работе рассмотрены эффективные метод и алгоритм создания ACMP-видео на GPU с помощью технологии рендеринга в текстуру, которые позволяют синтезировать панорамы c постоянной ориентацией или с привязкой к направлению взгляда наблюдателя. Также в исследовании предложены эффективные методы и алгоритмы воспроизведения ACMP-видео, основанные на визуализации видимых граней куба и адаптивной буферизации кадров. Полученные методы и алгоритмы реализованы в программном комплексе синтеза ACMP-видео (С++, OpenGL, FFmpeg), который включает в себя модуль захвата кадров (встраиваемый в СВО) и плеер. Разработанное решение было протестировано в системе «Виртуальная Земля» по обучению наблюдению объектов земной поверхности с орбиты Международной космической станции (МКС). С помощью модуля захвата было создано ACMP-видео полета вдоль участка подспутниковой трассы МКС. При воспроизведении данного видео обучаемый летит по орбите над виртуальной 3D поверхностью Земли и может исследовать ее, поворачивая камеру. Апробация комплекса подтвердила адекватность разработанных методов и алгоритмов поставленной задаче. Полученные научные и практические результаты позволяют расширить возможности и сферу применения СВО, систем научной визуализации, видеотренажеров и виртуальных лабораторий, эффективно обмениваться опытом между исследователями и др.Ключевые слова: видео 360 градусов; панорамное видео; кубическая проекция; визуализация; виртуальное окружение.

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A Low-Complexity Video Encoder for Equirectangular Projected 360 Video Content

Cited by 23 publications

References 9 publications

Exploring ERP Distortions to Reduce the Encoding Time of 360 Videos

Exploring ERP Distortions to Reduce the Encoding Time of 360 Videos

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

Efficient methods and algorithms to synthesize 360-degree video based on cubemap projection of virtual environment

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