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Affine Transformation-Based Generative Face Video compression

Affine Transformation-Based Generative Face Video Compressio...

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2025 data compression conference, dcc 2025

作者： Lin, Xihua Song, Xiaodan Zuo, Xuguang Li, Xuyang Gao, Dahua Xie, Xuemei Shi, Guangming Guangzhou Institute of Technology Xidian University China Pazhou Lab Huangpu China NETINT Technologies Canada School of Artificial Intelligence Xidian University China

ISBN: (纸本)9798331534714

In this paper, we propose a generative face video compression framework based on affine transformations to better represent large movements without parameter transmission. It mainly consists of an encoder and decoder, and our encoder is similar to the one in [1]. Intra frame are compressed by the existing encoder, while subsequent inter frames are compressed into compact inter frame features. In the decoder, feature alignment is first established to map the decoded intra frame and inter frame features into the same domain. The aligned features are then combined with the appearance features extracted by the appearance encoder from the intra frame and fed into the coarse-fine affine transform module to establish motion estimation and compensation. The coarse affine transform focuses on global motion, while the fine affine transform deals with local motion, such as lip motion. Finally, the transformed features are fed into the image generation module to obtain the final reconstruction results. © 2025 IEEE.

关键词： Affine transforms

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PCGCD: Joint Point Cloud Geometry compression and Denoising

PCGCD: Joint Point Cloud Geometry Compression and Denoising

引用

2025 data compression conference, dcc 2025

作者： Meng, Lixuan Ou, Ronghuang Zhang, Qi Liu, Shan Li, Ge Peking University Shenzhen Graduate School Guangdong Provincial Key Laboratory of Ultra High DefnitionImmersive Media Technology Shenzhen China Pengcheng Laboratory Shenzhen China Tencent Media Laboratory Palo Alto United States

ISBN: (纸本)9798331534714

With the rapid growth in the scale of point cloud data, lossy point cloud compression has become increasingly important for data storage and transmission. However, most existing point cloud compression methods assume that the input point clouds are noise-free. In practical applications, point clouds captured by 3D scanners or LiDAR sensors are often affected by noise, which significantly hinders subsequent point cloud processing and analysis. When applying existing compression algorithms directly to noisy point clouds, their performance deteriorates drastically. To address this issue, this paper explores a more challenging yet practical task: lossy geometry compression for point clouds with various noise levels. In this paper, we propose a novel approach, termed PCGCD, which jointly optimizes point cloud geometry compression and denoising. We first design a learning strategy for joint compression and denoising, enabling the basic mapping process from 'noisy' point clouds to 'clean' ones. Furthermore, we introduce a Prompt Guidance Module (PGM) embedded in the decoder to guide the reconstruction of high-quality point clouds, achieving noise-adaptive decoding and denoising. Extensive experiments conducted in both synthetic and real-world noise scenarios validate the effectiveness of our approach, showing significant improvements compared to baseline methods. © 2025 IEEE.

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Adaptive Rate Control for Deep Video compression with Rate-Distortion Prediction

Adaptive Rate Control for Deep Video Compression with Rate-D...

引用

2025 data compression conference, dcc 2025

作者： Gu, Bowen Chen, Hao Lu, Ming Yao, Jie Ma, Zhan Nanjing University China T-Head Semiconductor Co. Ltd. China

ISBN: (纸本)9798331534714

Deep video compression has made significant progress in recent years, achieving rate-distortion performance that surpasses that of traditional video compression methods. However, rate control schemes tailored for deep video compression have not been well studied. In this paper, we propose a neural network-based $\lambda$ -domain rate control scheme for deep video compression, which determines the coding parameter $\lambda$ for each to-be-coded frame based on the rate-distortion- $\lambda\ (\mathrm{R}-\mathrm{D}-\lambda)$ relationships directly learned from uncompressed frames, achieving high rate control accuracy efficiently without the need for pre-encoding. Moreover, this content-aware scheme is able to mitigate inter-frame quality fluctuations and adapt to abrupt changes in video content. Specifically, we introduce two neural network-based predictors to estimate the relationship between bitrate and $\lambda$, as well as the relationship between distortion and $\lambda$ for each frame. Then we determine the coding parameter $\lambda$ for each frame to achieve the target bitrate. Experimental results demonstrate that our approach achieves high rate control accuracy at the mini-GOP level with low time overhead and mitigates inter-frame quality fluctuations across video content of varying resolutions. © 2025 IEEE.

关键词： Network coding

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RNA secondary structures: from ab initio prediction to better compression, and back

RNA secondary structures: from ab initio prediction to bette...

引用

data compression conference (dcc)

作者： Evarista Onokpasa Sebastian Wild Prudence W.H. Wong University of Liverpool UK

In this paper, we use the biological domain knowledge incorporated into stochastic models for ab initio RNA secondary-structure prediction to improve the state of the art in joint compression of RNA sequence and structure data (Liu et al., BMC Bioinformatics, 2008). Moreover, we show that, conversely, compression ratio can serve as a cheap and robust proxy for comparing the prediction quality of different stochastic models, which may help guide the search for better RNA structure prediction models. Our results build on expert stochastic context-free grammar models of RNA secondary structures (Dowell & Eddy, BMC Bioinformatics, 2004; Nebel & Scheid, Theory in Biosciences, 2011) combined with different (static and adaptive) models for rule probabilities and arithmetic coding. We provide a prototype implementation and an extensive empirical evaluation, where we illustrate how grammar features and probability models affect compression ratios.

关键词： Adaptation models Correlation RNA Biological system modeling Stochastic processes Predictive models data models

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AIDetx: A compression-Based Method for Identification of Machine-Learning Generated Text

AIDetx: A Compression-Based Method for Identification of Mac...

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2025 data compression conference, dcc 2025

作者： Almeida, Leonardo Rodrigues, Pedro Magalhaes, Diogo Pinho, Armando J. Pratas, Diogo IEETA/LASI - Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Aveiro3810-193 Portugal University of Aveiro DETI - Department of Electronics Telecommunications and Informatics Aveiro3810-193 Portugal University of Helsinki DoV - Department of Virology Helsinki00014 Finland

ISBN: (纸本)9798331534714

We present AIDetx, a framework for detecting AI-generated text using finite-context models (FCMs) by classifying human and AI-generated text. Unlike deep learning classifiers, which are resource-intensive and less interpretable, AIDetx builds separate compression models for human and AI-generated text (shown in Fig 1), classifying new inputs by determining which model compresses the text more effectively. Evaluations on the HC3 and AI-human-text datasets showed strong performance, achieving F1 scores of 97% and 99%, with notable computational efficiency, no need for GPUs, while offering enhanced interpretability compared to deep learning models. © 2025 IEEE.

关键词： Deep learning

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Adaptive Occupancy Map Guided Neural Representation for Video Based Point Cloud Attribute compression

Adaptive Occupancy Map Guided Neural Representation for Vide...

引用

2025 data compression conference, dcc 2025

作者： Chi, Yirong Han, Cheng Lu, Shiyu Luo, Hao Changchun University of Science and Technology Changchun130000 China

ISBN: (纸本)9798331534714

The advancement of video-based point cloud(V-PCC) is essential for the widespread adoption of point cloud technology. However, it faces significant challenges, including the occupancy map (OM) containing numerous empty areas due to improper patch packaging, and serious compression time-consuming. Excessive empty areas can burden the subsequent video compression. Prolonged processing times may hinder the widespread adoption of V-PCC. Thus we propose data-adaptive patch packing (DAPP) to reduce empty areas during the projection process. In addition, considering the progressive nature of neural representations for videos, we propose OM-guided neural representations for videos (OM-NeRV), which utilizes advanced neural representation techniques to compress video effectively. First, it utilizes the OM to enable the network to prioritize processing valuable data when representing point clouds. Next, by utilizing context features, the residual motion features are combined with the current frame's features, thereby enhancing the network's representational capacity. Experiments have shown that our method outperforms the latest proposed advanced methods in terms of attribute rate-distortion performance and time efficiency. © 2025 IEEE.

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OPIRC: An Output-Interleaved Range Coding Algorithm

OPIRC: An Output-Interleaved Range Coding Algorithm

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2025 data compression conference, dcc 2025

作者： Wang, Na Ma, Chenchao Lin, Sian-Jheng Shao, Shuo Qin, Chuan School of Optoelectronic Information and Computer Engineering University of Shanghai for Science and Technology Shanghai China School of Cyber Science and Technology University of Science and Technology of China Hefei China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China

ISBN: (纸本)9798331534714

Range coding is a type of entropy coding widely used in modern data compressors. However, its compression and decompression processes involve multiple range adjustments, and the bitstream can only be read sequentially during decoding, resulting in quite high latency. In addition, existing input-interleaved fast implementations demand additional computational and memory overhead for the post-compression byte-swizzling step, which leads to increased compression time. In this paper, we propose a parallel range coding method that employs multiple encoders and decoders without the need for the swizzling step. It is achieved by designing a sliding window mechanism to interleave the outputs of multiple encoders, so that the positions of each encoder's outputs in the bitstream follow a predictable and ordered pattern. This design reduces encoding latency and enables each decoder to pre-locate the data it needs to read during decoding, thereby improving both compression and decompression performance. The simulation results indicate that compared to the traditional range coding and existing multi-way input-interleaved implementations (which require a large amount of memory overhead during encoding), our proposal achieves an average throughput increase of 48.03%/81.08% and 74.01%/9.14% during encoding/decoding, respectively, with almost the same compression ratios. © 2025 IEEE.

关键词： data compression ratio

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DeepFGS: Fine-Grained Scalable Coding for Learned Image compression

DeepFGS: Fine-Grained Scalable Coding for Learned Image Comp...

引用

2025 data compression conference, dcc 2025

作者： Zhai, Yongqi Ma, Yi Tang, Luyang Jiang, Wei Wang, Ronggang Shenzhen Graduate School Peking University Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology China Pengcheng Laboratory China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9798331534714

Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment. However, most existing scalable compression methods face two challenges: reduced compression performance and insufficient scalability. To overcome the above problems, this paper proposes a learned fine-grained scalable image compression framework, namely DeepFGS. Specifically, we introduce a feature separation backbone to divide the image information into basic and scalable features, then redistribute the features channel by channel through an information rearrangement strategy. In this way, we can generate a continuously scalable bitstream via one-pass encoding. For entropy coding, we design a mutual entropy model to fully explore the correlation between the basic and scalable features. In addition, we reuse the decoder to reduce the parameters and computational complexity. Experiments demonstrate that our proposed DeepFGS outperforms previous learning-based scalable image compression models and traditional scalable image codecs in both PSNR and MS-SSIM metrics. © 2025 IEEE.

关键词： Image compression

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Learned compression for Compressed Learning

Learned Compression for Compressed Learning

引用

2025 data compression conference, dcc 2025

作者： Jacobellis, Dan Yadwadkar, Neeraja J. University of Texas at Austin AustinTX78712 United States

ISBN: (纸本)9798331534714

Modern sensors produce increasingly rich streams of high-resolution data. Due to resource constraints, machine learning systems discard the vast majority of this information via resolution reduction. Instead, compressed-domain learning allows models to operate on compact latent representations. However, existing compression strategies are ill-suited for compressed learning. Linear transform coding and end-to-end learned compression systems reduce bitrate, but do not uniformly reduce dimensionality. Thus, they still need high compute. Generative autoencoders reduce dimensionality, but their adversarial or perceptual objectives result in loss of detail, leading to decreased model quality. To address these limitations, we introduce WaLLoC (Wavelet Learned Lossy compression), a neural codec architecture that combines linear transform coding with nonlinear dimensionality-reducing autoencoders. WaLLoC sandwiches a shallow, asymmetric autoencoder and entropy bottle-neck between an invertible wavelet packet transform. Across several key metrics, WaLLoC outperforms the autoencoders used in state-of-the-art latent diffusion models. WaLLoC does not require perceptual or adversarial losses to represent high-frequency detail, providing compatibility with modalities beyond RGB images and stereo audio. WaLLoC's encoder consists almost entirely of linear operations, making it exceptionally efficient and suitable for mobile computing and remote sensing. It also enables learning directly from compressed data. We demonstrate WaLLoC's capability for compressed-domain learning across several tasks, including image classification, colorization, document understanding, and music source separation. Our code, experiments, and pre-trained audio and image codecs are available at https://***/walloc/. © 2025 IEEE.

关键词： Image coding

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CST-PCAC: Learned Point Cloud Attribute compression with Cross-Scale Point Transformer

CST-PCAC: Learned Point Cloud Attribute Compression with Cro...

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2025 data compression conference, dcc 2025

作者： Huo, Xiao Zhang, Wei Yang, Fuzheng Xidian University Xi'an China Pengcheng Laboratory Shenzhen China

ISBN: (纸本)9798331534714

Point clouds serve as a fundamental format for virtual and augmented reality applications. However, their substantial data volumes present considerable challenges regarding data storage and transmission. This paper introduces a novel point transformer-based approach for point cloud attribute compression, termed as CST-PCAC, which utilizes a transformer architecture designed to exploit correlations across multiple scales. Our method is grounded in a variational autoencoder framework, enhanced by stacked sparse convolution (SConv) and the proposed Cross-Scale Point Transformer (CSFormer). CSFormer incorporates two types of attention mechanisms: Within-Scale Attention (WSA) and Cross-Scale Attention (CSA). WSA constructs attention maps within K-nearest neighbours at the current scale, while CSA operates across adjacent upper or lower scales. By leveraging varied sparsity distributions and the geometry distortions inherent in consecutive scales, CSFormer enhances the receptive field and feature representation, thereby effectively extracting correlations to minimize redundancy. Experimental results demonstrate that CST-PCAC achieves average Bjontegaard Delta bitrate gains exceeding 30%, 10%, and 20% in comparison to Sparse-PCAC, Scalable-PCAC, and G-PCC, respectively. In terms of computational efficiency, CST-PCAC exhibits a time complexity two orders of magnitude lower than that of deep learning-based methods including Sparse-PCAC and Scalable-PCAC, while maintaining a comparable complexity to traditional methods like G-PCC. © 2025 IEEE.

关键词： Deep learning

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