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Feature Prediction for 3D Gaussian Splatting compression

Feature Prediction for 3D Gaussian Splatting Compression

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2025 data compression conference, dcc 2025

作者： Tang, Luyang Zhai, Yongqi Yang, Jiayu Yang, Chunhui Wang, Ronggang Shenzhen Graduate School Peking University Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology China Pengcheng Laboratory China

ISBN: (纸本)9798331534714

Recently, 3D Gaussian Splatting (3DGS) has emerged as a promising scene representation technique for novel view synthesis. However, the large number of Gaussians used to represent the 3D scene poses challenges for storage and transmission. Existing advanced compression methods focus on designing various context models for entropy modeling, but lack the exploration of prediction techniques. In this paper, we dig into feature correlations in the anchor-based Gaussian representation and propose two types of feature prediction techniques to further reduce the scene redundancy. Firstly, we observe that context features contain rich scene priors, which are also helpful for reconstruction but often ignored by previous methods. To this end, we design a Context-based Weighted Prediction module to adaptively aggregate the anchor feature and the context feature for rendering, which can reduce the storage costs in anchors. Secondly, a high degree of similarity is discovered between different feature channels. To utilize the cross-channel correlations, we propose a Cross-channel Residual Prediction module, which further reduces the bit cost for coding anchor features. Extensive experiments show that our method can further enhance compression performance while maintaining rendering quality compared to existing 3DGS compression methods. Our code is available at https://***/Pomelomm/FP-GS. © 2025 IEEE.

关键词： Gaussian distribution

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LL-ICM: Image compression for Low-Level Machine Vision via Large Vision-Language Model

LL-ICM: Image Compression for Low-Level Machine Vision via L...

引用

2025 data compression conference, dcc 2025

作者： Xue, Yuan Zhang, Qi Jia, Chuanmin Wang, Shiqi School of Computer Science City University of Hong Kong Hong Kong Peking University National Engineering Research Center of Visual Technology China Wangxuan Institute of Computer Technology Peking University China

ISBN: (纸本)9798331534714

Image compression for Machines (ICM) aims to compress images for machine vision tasks, while current methods mostly focus on the demands for high-level tasks. However, the quality of original images is usually not guaranteed in the real world, leading to even worse downstream task performance after compression. Thus, lowlevel (LL) restoration tasks should also be considered in ICM. In this paper, we propose the first ICM framework for LL machine vision tasks, namely LL-ICM, which optimizes the compression and LL processing performance simultaneously. Moreover, LL-ICM leverages large vision-language model (VLM) to solve different LL task within a single model, which is particularly useful when the distortion type of the original image is uncertain. As illustrated in Fig. 1(a), LL-ICM consists of a neural image codec and a VLM-based LL processing module. Given an original image with distortions, LL-ICM firstly compress it as X. Then, we extract a generalized feature F from X , which is then encoded as two representations, distortion type f and caption s. After that, the LL processing module receives X and its representations to generate the restored version of X, i.e., XH. © 2025 IEEE.

关键词： Image compression

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Neural Image compression with Multi-Scale Depthwise Separable Dilated Convolution and Multi-Distribution Mixture Entropy Model

Neural Image Compression with Multi-Scale Depthwise Separabl...

引用

2025 data compression conference, dcc 2025

作者： Yang, Dongjian Fan, Xiaopeng Meng, Xiandong Zhao, Debin Harbin Institute of Technology Guangdong Shenzhen518055 China PengCheng Laboratory Guangdong Shenzhen518055 China

ISBN: (纸本)9798331534714

Recently, neural image compression (NIC) has made remarkable progress. Two key parts of NIC are the encoder-decoder and the entropy model. For the encoder-decoder, a larger effective receptive field (ERF) means a stronger transformation ability. Existing methods usually enlarge the ERF at the expense of complexity, which is intolerable. To address this issue, we propose a multi-scale depthwise separable dilated convolution (MSDSDC) to build the encoder-decoder. Specifically, we first construct a depthwise separable dilated convolution (DSDC) by using the depthwise separable strategy in dilated convolution to reduce its complexity. Subsequently, multi-scale features extracted by three DSDCs with varying dilation rates are fused to expand the ERF of the encoder-decoder, consequently enhancing its transformation capability. Besides, we design a multi-distribution mixture entropy model (MDMEM) to further enhance the flexibility of latent representation probability modeling. The experimental results demonstrate that our proposed method achieves the best balance between rate-distortion performance and complexity. © 2025 IEEE.

关键词： Image compression

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Dynamic Temporal Reference Aggregation for Neural Video compression

Dynamic Temporal Reference Aggregation for Neural Video Comp...

引用

2025 data compression conference, dcc 2025

作者： Liao, Shuhong Feng, Kexiang Huang, Zhimeng Ma, Siwei Wang, Qi Chen, Lili Jia, Chuanmin Peking University Beijing China University of Chinese Academy of Sciences Beijing China Institute of Computing Technology CAS Beijing China MIGU Video Co. Ltd. Beijing China

ISBN: (纸本)9798331534714

Neural Video compression (NVC) has advanced significantly in recent years, with improvements in inter prediction techniques. In inter prediction, most NVC approaches utilize pixel information or temporal features from neighboring frames as reference information, while using optical flow to represent motion information. In this paper, we introduce an innovative and efficient method for Dynamic Temporal Reference Aggregation (DTRA). The proposed DTRA consists of two components: Temporal Information Compensation (TIC) and Feature Level Motion Information Enhancement (MIE). The TIC module generates compensation information by leveraging long-term temporal information from the decoding buffer, enriching the semantic content of the reference features and enhancing their texture details. The MIE module refines the motion features at the encoder side and divides the motion information into multiple groups for diverse motion alignment at the decoder side, thereby improving the motion compensation. Extensive experiments demonstrate the effectiveness of the proposed method, achieving an average bitrate savings of 9.67% compared to state-of-the-art (SOTA) approaches. © 2025 IEEE.

关键词： Image compression

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Computing Consecutively Maximal Periodic Patterns Over APT Compressed data

Computing Consecutively Maximal Periodic Patterns Over APT C...

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2025 data compression conference, dcc 2025

作者： Levy, Avivit Shapira, Dana Shenkar College of Engineering.Design.Art Ramat-Gan52526 Israel Ariel University Ariel40700 Israel

ISBN: (纸本)9798331534714

The Arithmetic Progressions Tree (APT) is a data structure storing an encoding of a monotonic sequence $\mathcal{L}$ in $[1..n]$. While previous work on $\mathsf{APT}$ focused on its theoretical and experimental compression guarantees, recently, it was shown that searches of sub-sequences, runs and periodic patterns over the $\mathsf{APT}$ compressed data can be applied. This paper extends the set of supported operations and focuses on the computation of consecutively maximal periodic patterns directly over the APT. In particular, given the $\mathsf{APT}$ compressed representation of $\mathcal{L}$, we show how: (1)One can find if a consecutive periodic pattern with difference $d_{P}$ is represented by an $\mathsf{APT}$ node in time $O(\log n)$ and if positive, report its occurrences in $\mathcal{L}$ in time proportional to the output size multiplied by $\log d_{P}$ and the size of the $\mathsf{APT}$ compressed representation of $\mathcal{L}$, while assuring that every reported consecutive occurrence is consecutively maximal. (2)Given a query periodic pattern difference, $d_{P}$, we can give a one-sided $O(\log d_{P})$ -additive approximation for the length of the consecutively maximal periodic pattern with difference $d_{P}$ that occurs in $\mathcal{L}$ in time $O(\log n)$. (3)We give a one-sided $O(\log n)$ -additive approximation for the maximum length of a consecutively maximal periodic pattern that occurs in $\mathcal{L}$ in time $O(\sqrt{n}\log n)$. © 2025 IEEE.

关键词： Digital arithmetic

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Regularized Deep Convolutional Encoding-Decoding Generative Network for Remote Sensing Cloud Image compression

Regularized Deep Convolutional Encoding-Decoding Generative ...

引用

2025 data compression conference, dcc 2025

作者： Wang, Yu Yang, Jing Wang, Sijia Yang, Xingli The School of Modern Educational Technology Shanxi University Taiyuan030006 China The School of Mathematics and Statistics Shanxi University Taiyuan030006 China The School of Computer and Information Technology Shanxi University Taiyuan030006 China

ISBN: (纸本)9798331534714

Typically, meteorological cloud observations are often presented in the form of high-quality and high-resolution images, such as satellite remote sensing cloud images and ground-based remote sensing cloud images. However, faced with such a large number of high-resolution images, even for the existing GPU servers and parallel computing with high-speed data processing capabilities, the computational cost of image recognition and analysis is very large, or even unacceptable. A widely used method is to resize the original image to a version with a decreased resolution and to obtain cloud information from this decreased image. Clearly, image compression is crucial. Universal interpolation based image compression methods compress the image size by directly clipping unimportant pixels at the pixel level, which may fail to capture important image feature information such as image structure, resulting in low image recognition performance. In particular, the popular deep encoding-decoding generative network can perfectly reconstruct the image almost identical to the original image by self-supervised learning the useful high-resolution image feature representation. Thus, in this paper, a remote sensing cloud image compression method based on regularized deep convolutional encoding-decoding generation network is proposed by constraining encoding-decoding network and the interpolation method. The proposed method extracts useful feature representations from the original image through an encoding process with a convolutional neural network model, and then reconstructs the compressed image at a specified compression ratio through a decoding process with a deconvolutional neural network model. In the proposed method, a Kullback-Leibler (KL) divergence constraint is introduced to align the feature representation distribution with a standard normal distribution, enhancing the accuracy of the feature representation. Additionally, the regularization constraint between the feature maps o

关键词： Image compression

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Beyond GFVC: A Progressive Face Video compression Framework with Adaptive Visual Tokens

Beyond GFVC: A Progressive Face Video Compression Framework ...

引用

2025 data compression conference, dcc 2025

作者： Chen, Bolin Yin, Shanzhi Zhang, Zihan Chen, Jie Liao, Ru-Ling Zhu, Lingyu Wang, Shiqi Ye, Yan City University of Hong Kong Hong Kong DAMO Academy Alibaba Group China

ISBN: (纸本)9798331534714

Recently, deep generative models have greatly advanced the progress of face video coding towards promising rate-distortion performance and diverse application functionalities. Beyond traditional hybrid video coding paradigms, Generative Face Video compression (GFVC) relying on the strong capabilities of deep generative models and the philosophy of early Model-Based Coding (MBC) can facilitate the compact representation and realistic reconstruction of visual face signal, thus achieving ultra-low bitrate face video communication. However, these GFVC algorithms are sometimes faced with unstable reconstruction quality and limited bitrate ranges. To address these problems, this paper proposes a novel Progressive Face Video compression framework, namely PFVC, that utilizes adaptive visual tokens to realize exceptional trade-offs between reconstruction robustness and bandwidth intelligence. In particular, the encoder of the proposed PFVC projects the high-dimensional face signal into adaptive visual tokens in a progressive manner, whilst the decoder can further reconstruct these adaptive visual tokens for motion estimation and signal synthesis with different granularity levels. Experimental results demonstrate that the proposed PFVC framework can achieve better coding flexibility and superior rate-distortion performance in comparison with the latest Versatile Video Coding (VVC) codec and the state-of-the-art GFVC algorithms. The project page can be found at https://***/Berlin0610/PFVC. © 2025 IEEE.

关键词： Image coding

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L-LBVC: Long-Term Motion Estimation and Prediction for Learned Bi-Directional Video compression

L-LBVC: Long-Term Motion Estimation and Prediction for Learn...

引用

2025 data compression conference, dcc 2025

作者： Zhai, Yongqi Tang, Luyang Jiang, Wei Yang, Jiayu Wang, Ronggang Shenzhen Graduate School Peking University Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology China Pengcheng Laboratory China

ISBN: (纸本)9798331534714

Recently, learned video compression (LVC) has shown superior performance under lowdelay configuration. However, the performance of learned bi-directional video compression (LBVC) still lags behind traditional bi-directional coding. The performance gap mainly arises from inaccurate long-term motion estimation and prediction of distant frames, especially in large motion scenes. To solve these two critical problems, this paper proposes a novel LBVC framework, namely L-LBVC. Firstly, we propose an adaptive motion estimation module that can handle both short-term and long-term motions. Specifically, we directly estimate the optical flows for adjacent frames and non-adjacent frames with small motions. For non-adjacent frames with large motions, we recursively accumulate local flows between adjacent frames to estimate long-term flows. Secondly, we propose an adaptive motion prediction module that can largely reduce the bit cost for motion coding. To improve the accuracy of long-term motion prediction, we adaptively downsample reference frames during testing to match the motion ranges observed during training. Experiments show that our L-LBVC significantly outperforms previous state-of-the-art LVC methods and even surpasses VVC (VTM) on some test datasets under random access configuration. © 2025 IEEE.

关键词： Video streaming

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FAPC: Frequency-Based Adaptive Pixel Correction for Compressed Screen Content

FAPC: Frequency-Based Adaptive Pixel Correction for Compress...

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2025 data compression conference, dcc 2025

作者： Song, Zetian Zhang, Jiaqi Jia, Chuanmin Wang, Qi Chen, Lili Ma, Siwei Gao, Wen School of Computer Science Peking University National Engineering Research Center of Visual Technology China Wangxuan Institute of Computer Technology Peking University China MIGU Video Co. Ltd. China

ISBN: (纸本)9798331534714

Screen content is an important category of video. The statistical distribution of pixels in screen content exhibits substantial differences compared to camera-captured video, leading to different compression needs and challenges. Hitherto, most Screen Content Coding (SCC) tools are designed for block-based hybrid coding frameworks, which may not be suitable for emerging wavelet-based and learning-based coding frameworks. Consequently, a plug-and-play SCC tool independent of coding frameworks is lacking in the current video coding landscape. In this paper, an out-loop coding method, Frequency-based Adaptive Pixel Correction (FAPC), is proposed to improve the SCC performance for arbitrary codecs. First, a True Color Value (TCV) table is established based on the most frequently occurring pixel values. Then, the reconstructed pixels are corrected according to the TCV table. To realize precise pixel correction, an adaptive threshold derivation method is meticulously designed to control the pixel correction process. Furthermore, an inheritance coding strategy is proposed to reduce the overhead of parameter transmission. The proposed method has been integrated into three different coding frameworks. Simulation results demonstrate that the proposed method can achieve 10.66%, 3.55% and 10.67% luma component BD-BR gains on the three coding frameworks, respectively. These results prove the superiority and universality of the proposed method. © 2025 IEEE.

关键词： Image compression

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A high efficient cascade coder with predictor blending method for lossless audio compression

A high efficient cascade coder with predictor blending metho...

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2020 data compression conference, dcc 2020

作者： Ulacha, Grzegorz Wernik, Cezary West Pomeranian University of Technology in Szczecin Zolnierska 49 Szczecin71-210 Poland

ISBN: (纸本)9781728164571

Based on the idea of RLS-LMS cascade of predictors as in MPEG-4 lossless audio coder we developed our own improved cascade of predictors with backward adaptation and new predictor blending method. In carried out experiment comparing the results with other known lossless audio coders, our method obtained the best efficiency. © 2020 IEEE.

关键词： Digital arithmetic

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