Highly Efficient Multiview Depth Coding Based on Histogram Projection and Allowable Depth Distortion

Zhang, Yun; Zhu, Linwei; Hamzaoui, Raouf; Ho, Yo‐Sung

doi:10.1109/tip.2020.3036760

Cited by 13 publications

(6 citation statements)

References 41 publications

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“…Yang et al [35] recommended a cross-view multi-lateral filtering scheme, which enhances the quality of depth map using color and depth priors from adjacent views at different time-slots. To reduce the complexity in coding mode selection, Zhang et al [39] proposed an efficient MVD scheme based on depth histogram projection and allowable depth distortion. Lin et al [20] proposed to accelerate 3D-HEVC deep intra-frame coding using the characteristics of the human visual system.…”

Section: Multi-view Video Codingmentioning

confidence: 99%

Multi-View Video Coding with GAN Latent Learning

Lan¹,

Luo²,

Yan³

et al. 2022

Preprint

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The introduction of multiple viewpoints inevitably increases the bitrates to store and transmit video scenes. To reduce the compressed bitrates, researchers have developed to skip intermediate viewpoints during compression and delivery, and finally reconstruct them with Side Information (SI). Generally, the depth maps can be utilized to construct SI; however, it shows inferior performance with inaccurate reconstruction or high bitrates. In this paper, we propose a multi-view video coding based on SI of Generative Adversarial Network (GAN). At the encoder, we construct a spatio-temporal Epipolar Plane Image (EPI) and further utilize convolutional network to extract the latent code of GAN as SI; while at the decoder side, we combine the SI and adjacent viewpoints to reconstruct intermediate views by the generator of GAN. In particular, we set a joint encoder constraint of reconstruction cost and SI entropy, in order to achieve an optimal tradeoff between reconstruction quality and bitrate overhead. Experiments show a significantly improved Rate-Distortion (RD) performance compared with the state-of-the-art methods.

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Section: Multi-view Video Codingmentioning

confidence: 99%

Multi-View Video Coding with GAN Latent Learning

Lan¹,

Luo²,

Yan³

et al. 2022

Preprint

Self Cite

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“…With the urgent need for video that is free of viewpoint restrictions, video encoding control incorporating depth information has become a promising technology that can provide information with more degrees of freedom in virtual video content [157]- [159]. Existing multiview coding (MVC) and multiview plus depth (MVD) standards, such as MV-HEVC and 3D-HEVC, are traditional depth coding solutions that can compress images from different angles captured simultaneously by multiple cameras.…”

Section: E Rdo and Rc In Depth Codingmentioning

confidence: 99%

Recent Advances in Rate Control: From Optimization to Implementation and Beyond

Wei¹,

Zhou²,

Wang³

et al. 2022

Preprint

Self Cite

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Video coding is a video compression technique that compresses the original video sequence to achieve a smaller archive file or a lower transmission bandwidth under constraints on the visual quality loss. Rate control (RC) plays a critical role in video coding. It can achieve stable stream output in practical applications, especially in real-time video applications such as video conferencing or game live streaming. Most RC algorithms directly or indirectly characterize the relationship between the bit rate (R) and quantization (Q) and then allocate bits for every coding unit to guarantee the global bit rate and video quality level. This paper comprehensively reviews the classic RC technologies used in international video standards of past generations, analyses the mathematical models and implementation mechanisms of various schemes, and compares the performance of recent state-of-the-art RC algorithms. Finally, we discuss future directions and new application areas for RC methods. We hope that this review can help support the development, implementation, and application of RC in new video coding standards.

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“…Based on this, the selection process of the division depth in inter-frame CU, which has the highest computational complexity, has been continuously optimised by related research. According to the difference of the methods used in those algorithms, the fast selection algorithms of inter-frame CU depth can be roughly divided into two categories: traditional [12][13][14][15][16][17][18][19][20] and machine learning based methods [21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

“…There are also some methods that make good use of the characteristics of the human visual system. By using the above information to judge the current CU division depth in advance [16][17][18][19][20], the inter-frame coding process is further optimised.…”

Section: Traditional Fast Inter-frame Encoding Algorithmsmentioning

confidence: 99%

Fast 3D‐HEVC inter coding using data mining and machine learning

Zhang

Jia

et al. 2022

IET Image Processing

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The Three-Dimensional High Efficiency Video Coding standard is a video compression standard developed based on the two-dimensional video coding standard HEVC and used to encode multi-view plus depth format video. This paper proposes an algorithm based on eXtreme Gradient Boosting to solve the problem of high inter-frame coding complexity in 3D-HEVC. Firstly, explore the correlation between the division depth of the inter-frame coding unit and the texture features in the map, as well as the correlation between the coding unit division structure between each map and each viewpoint. After that, based on the machine learning method, a fast selection mechanism for dividing the depth range of the inter-frame coding tree unit based on the eXtreme Gradient Boosting algorithm is constructed. Experimental results show that, compared with the reference software HTM-16.0, this method can save an average of 35.06% of the coding time, with negligible degradation in terms of coding performance. In addition, the proposed algorithm has achieved different degrees of improvement in coding performance compared with the related works.

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Highly Efficient Multiview Depth Coding Based on Histogram Projection and Allowable Depth Distortion

Abstract: China. His research interests are video compression, 3D video processing, visual perception and machine learning.

Cited by 13 publications

References 41 publications

Multi-View Video Coding with GAN Latent Learning

Multi-View Video Coding with GAN Latent Learning

Recent Advances in Rate Control: From Optimization to Implementation and Beyond

Fast 3D‐HEVC inter coding using data mining and machine learning

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