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A Versatile Point Cloud Compressor Using Universal Multiscale conditional coding - Part I: Geometry

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第1期47卷 269-287页

作者： Wang, Jianqiang Xue, Ruixiang Li, Jiaxin Ding, Dandan Lin, Yi Ma, Zhan Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Jiangsu Peoples R China Hangzhou Normal Univ Sch Informat Sci & Engn Hangzhou 311121 Zhejiang Peoples R China Fudan Univ Dept Cardiovasc Surg Zhongshan Hosp Shanghai 200032 Peoples R China

A universal multiscale conditional coding framework, Unicorn, is proposed to compress the geometry and attribute of any given point cloud. Geometry compression is addressed in Part I of this paper, while attribute compression is discussed in Part II. We construct the multiscale sparse tensors of each voxelized point cloud frame and properly leverage lower-scale priors in the current and (previously processed) temporal reference frames to improve the conditional probability approximation or content-aware predictive reconstruction of geometry occupancy in compression. Unicorn is a versatile, learning-based solution capable of compressing static and dynamic point clouds with diverse source characteristics in both lossy and lossless modes. Following the same evaluation criteria, Unicorn significantly outperforms standard-compliant approaches like MPEG G-PCC, V-PCC, and other learning-based solutions, yielding state-of-the-art compression efficiency while presenting affordable complexity for practical implementations.

关键词： Attribute conditional coding geometry multiscale sparse representation point cloud compression

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LCCM-VC: LEARNED conditional coding MODES FOR VIDEO COMPRESSION

LCCM-VC: LEARNED CONDITIONAL CODING MODES FOR VIDEO COMPRESS...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hadizadeh, Hadi Bajic, Ivan V. Quchan Univ Technol Quchan Iran Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9798350302615

End-to-end learning-based video compression has made steady progress over the last several years. However, unlike learning-based image coding, which has already surpassed its hand-crafted counterparts, the progress on learning-based video coding has been slower. In this paper, we present learned conditional coding modes for video coding (LCCM-VC), a video coding model that competes favorably against HEVC reference software implementation. Our model utilizes conditional - rather than residual - coding, and introduces additional coding modes to improve compression performance. The compression efficiency is especially good in the high-quality/high-bitrate range, which is important for broadcast and video-on-demand streaming applications. The implementation of LCCM-VC is available at https://***/hadihdz/lccm_vc

关键词： End-to-end learned video coding conditional coding augmented normalizing flows autoencoders

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P-FRAME coding WITH GENERALIZED DIFFERENCE: A NOVEL conditional coding APPROACH 29

P-FRAME CODING WITH GENERALIZED DIFFERENCE: A NOVEL CONDITIO...

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IEEE International Conference on Image Processing (ICIP)

作者： Brand, Fabian Seiler, Juergen Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc D-91058 Erlangen Germany

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Motion compensated inter frame prediction is a common component of all video coders and greatly reduces temporal redundancy. With the rise of deep learning-based image and video compression, this concept has been successfully taken over from traditional coding approaches. These approaches offer a larger flexibility than traditional transform coding and therefore enable efficient conditional coding. In this work, we develop a novel conditional coding approach based on the generalized difference and generalized sum operators. This approach is a special case of a general conditional coder and has a very small complexity overhead. We also propose an extension which enables dynamic content-adaptive switching between conditional and residual coding. We show that the extended generalized difference coding outperforms both residual and conditional coding, saving 27.8% Bjontegaard delta rate compared to the former.

关键词： Video Compression conditional coding Autoencoder Deep Learning

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conditional Residual coding: A Remedy for Bottleneck Problems in conditional Inter Frame coding

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第7期34卷 6445-6459页

作者： Brand, Fabian Seiler, Juergen Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg FAU Chair Multimedia Commun & Signal Proc D-91058 Erlangen Germany

conditional coding is a new video coding paradigm enabled by neural-network-based compression. It can be shown that conditional coding is in theory better than the traditional residual coding, which is widely used in video compression standards like HEVC or VVC. However, on closer inspection, it becomes clear that conditional coders can suffer from information bottlenecks in the prediction path, i.e., that due to the data processing inequality not all information from the prediction signal can be passed to the reconstructed signal, thereby impairing the coder performance. In this paper we propose the conditional residual coding concept, which we derive from information theoretical properties of the conditional coder. This coder significantly reduces the influence of bottlenecks, while maintaining the theoretical performance of the conditional coder. We provide a theoretical analysis of the coding paradigm and demonstrate the performance of the conditional residual coder in a practical example. We show that conditional residual coders alleviate the disadvantages of conditional coders while being able to maintain their advantages over residual coders. In the spectrum of residual and conditional coding, we can therefore consider them as "the best from both worlds."

关键词： Encoding Decoding Channel coding Video compression Image reconstruction Image coding Standards conditional coding conditional autoencoder information theory

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On Benefits and Challenges of conditional Interframe Video coding in Light of Information Theory

On Benefits and Challenges of Conditional Interframe Video C...

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Picture coding Symposium (PCS)

作者： Brand, Fabian Seiler, Juergen Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Erlangen Germany

ISBN: (纸本)9781665492577

The rise of variational autoencoders for image and video compression has opened the door to many elaborate coding techniques. One example here is the possibility of conditional interframe coding. Here, instead of transmitting the residual between the original frame and the predicted frame (often obtained by motion compensation), the current frame is transmitted under the condition of knowing the prediction signal. In practice, conditional coding can be straightforwardly implemented using a conditional autoencoder, which has also shown good results in recent works. In this paper, we provide an information theoretical analysis of conditional coding for inter frames and show in which cases gains compared to traditional residual coding can be expected. We also show the effect of information bottlenecks which can occur in practical video coders in the prediction signal path due to the network structure, as a consequence of the data-processing theorem or due to quantization. We demonstrate that conditional coding has theoretical benefits over residual coding but that there are cases in which the benefits are quickly canceled by small information bottlenecks of the prediction signal.

关键词： video compression inter coding conditional coding information theory conditional autoencoder

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PFR-VC: Learning-Based Video Compression Framework with Predicted Frame Refinement

PFR-VC: Learning-Based Video Compression Framework with Pred...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhou, Zhidao Qiu, Hongxin Liu, Zhikai Sun, Wei Liang, Fan Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Learning-based video compression has attracted more and more attention in recent years. Traditional video coding relies on block-based motion estimation and spatial frequency transformation. While these techniques can effectively compress videos, further enhancing the compression ratio becomes challenging. Introducing deep learning methods can overcome the limitations of manually designed algorithms. In this paper, we propose a learning-based video compression framework with Predicted Frame Refinement (PFR) to improve the compression efficiency. Firstly, a simple autoencoder is introduced to encode the motion information, eliminating the need for a complex optical-flow network. Then, we design a predicted frame refinement network with an attention feature fusion mechanism to generate predicted frames more suitable for extracting context. Finally, we introduce a context coding scheme to improve the compression ratio by jointly utilizing temporal prior and hyper prior. The entire network can be globally optimized and trained from scratch. The experimental result shows that the proposed compression framework outperforms previous methods. Our approach brings 31.2% more saved bit rate than x265 with veryslow preset. Our model also achieves a 7.1% gain in Multi-Scale Structural Similarity Index Measure (MS-SSIM) compared with the recent method proposed by Guo et al.(2023).

关键词： Learned video compression end-to-end training approach predicted frame refinement conditional coding

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Learning-Based conditional Image Compression

Learning-Based Conditional Image Compression

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Shen, Tianma Peng, Wen-Hsiao Shih, Huang -Chia Liu, Ying Santa Clara Univ Dept Comp Sci & Engn Santa Clara CA 95053 USA Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan Yuan Ze Univ Dept Elect Engn Taoyuan Taiwan

ISBN: (纸本)9798350330991;9798350331004

In recent years, deep learning-based image compression has achieved significant success. Most schemes adopt an end-to-end trained compression network with a specifically designed entropy model. Inspired by recent advances in conditional video coding, in this work, we propose a novel transformer-based conditional coding paradigm for learned image compression. Our approach first compresses a low-resolution version of the target image and up-scales the decoded image using an off-the-shelf super-resolution model. The super-resolved image then serves as the condition to compress and decompress the target highresolution image. Experiments demonstrate the superior ratedistortion performance of our approach compared to existing methods.

关键词： conditional coding deep learning entropy model hyperprior image compression super resolution vision transformer

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Compressing 3D Gaussian Splatting via a Generalizable Neural Coder

Compressing 3D Gaussian Splatting via a Generalizable Neural...

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2024 Conference on Visual Communications and Image Processing

作者： Zhang, Junteng Chen, Tong Zhu, Hao Wang, Dong Ding, Dandan Ma, Zhan Nanjing Univ Nanjing Peoples R China OPPO Inc Nanjing Peoples R China Hangzhou Normal Univ Hangzhou Peoples R China

ISBN: (纸本)9798331529543;9798331529550

As a promising technique for 3D representation, 3D Gaussian Splatting (3DGS) offers fast rendering speed and high fidelity while generating large data volumes. This challenges storage and transmission, so an efficient compression solution is required. Existing implicit methods require pre-scene optimization (online), leading to a long optimization time. By contrast, this paper regards the 3DGS as a point cloud and pre-trains a generalizable (offline) neural coder for compression. After obtaining the 3DGS representation, we focus on the data compression process, which is friendly to applications already equipped with a PCC codec. The neural coder employed is extended from a typical AI-based point cloud compression method, which uses a multiscale and multistage framework to exploit spatial correlations across scales and stages for conditional coding. Experimental results show that our method significantly outperforms existing 3DGS representations without compromising fidelity, achieving more than 39x and 6.8x compression ratio compared to the original 3DGS and SOTA Scaffold-GS, respectively. More importantly, our approach does not require additional time to optimize the compression model.

关键词： 3D Gaussian Splatting Compression conditional coding

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On the Rate-Distortion-Complexity Trade-offs of Neural Video coding 26

On the Rate-Distortion-Complexity Trade-offs of Neural Video...

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26th International Workshop on Multimedia Signal Processing

作者： Chen, Yi-Hsin Ho, Kuan-Wei Benjak, Martin Ostermann, JOrn Peng, Wen-Hsiao Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan Leibniz Univ Hannover Hannover Germany

ISBN: (纸本)9798350387261;9798350387254

This paper aims to delve into the rate-distortion-complexity trade-offs of modern neural video coding. Recent years have witnessed much research effort being focused on exploring the full potential of neural video coding. conditional autoencoders have emerged as the mainstream approach to efficient neural video coding. The central theme of conditional autoencoders is to leverage both spatial and temporal information for better conditional coding. However, a recent study indicates that conditional coding may suffer from information bottlenecks, potentially performing worse than traditional residual coding. To address this issue, recent conditional coding methods incorporate a large number of high-resolution features as the condition signal, leading to a considerable increase in the number of multiply-accumulate operations, memory footprint, and model size. Taking DCVC as the common code base, we investigate how the newly proposed conditional residual coding, an emerging new school of thought, and its variants may strike a better balance among rate, distortion, and complexity.

关键词： Learned video compression conditional coding and conditional residual coding

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引用

IEEE-ACM TRANSACTIONS ON NETWORKING 2006年第1期14卷 41-54页

作者： Cristescu, RZ Beferull-Lozano, B Vetterli, M Wattenhofer, R CALTECH Ctr Math Informat Pasadena CA 91125 USA Swiss Fed Inst Technol Lab Audiovisual Commun CH-1015 Lausanne Switzerland Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA ETH Dept Comp Sci Distributed Comp Grp CH-8092 Zurich Switzerland

We consider the problem of correlated data gathering by a network with a sink node and a tree-based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy-based coding model with explicit communication where coding is simple and the transmission structure optimization is difficult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NP-hard. We propose some efficient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal.

关键词： conditional coding correlated data gathering distributed algorithms NP-completeness routing tree sensor networks traveling salesman

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