Image_Compression
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Inevitable Encounters: Backdoor Attacks Involving Lossy Compression
2026-03-14 09:45:59Qian Li, Yunuo Chen, Yuntian ChenAbstract
Real-world backdoor attacks often require poisoned datasets to be stored and transmitted before being used to compromise deep learning systems. However, in the era of big data, the inevitable use of lossy compression poses a fundamental challenge to invisible backdoor attacks. We find that triggers embedded in RGB images often become ineffective after the images are lossily compressed into binary bitstreams (e.g., JPEG files) for storage and transmission. As a result, the poisoned data lose its malicious effect after compression, causing backdoor injection to fail. In this paper, we highlight the necessity of explicitly accounting for the lossy compression process in backdoor attacks. This requires attackers to ensure that the transmitted binary bitstreams preserve malicious trigger information, so that effective triggers can be recovered in the decompressed data. Building on the region-of-interest (ROI) coding mechanism in image compression, we propose two poisoning strategies tailored to inevitable lossy compression. First, we introduce Universal Attack Activation, a universal method that uses sample-specific ROI masks to reactivate trigger information in binary bitstreams for learned image compression (LIC). Second, we present Compression-Adapted Attack, a new attack strategy that employs customized ROI masks to encode trigger information into binary bitstreams and is applicable to both traditional codecs and LIC. Extensive experiments demonstrate the effectiveness of both strategies.
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URL
https://arxiv.org/abs/2603.13864
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D-Compress: Detail-Preserving LiDAR Range Image Compression for Real-Time Streaming on Resource-Constrained Robots
2026-03-14 02:15:25Shengqian Wang, Chang Tu, He ChenAbstract
Efficient 3D LiDAR point cloud compression (LPCC) and streaming are critical for edge server-assisted robotic systems, enabling real-time communication with compact data representations. A widely adopted approach represents LiDAR point clouds as range images, enabling the direct use of mature image and video compression codecs. However, because these codecs are designed with human visual perception in mind, they often compromise geometric details, which downgrades the performance of downstream robotic tasks such as mapping and object detection. Furthermore, rate-distortion optimization (RDO)-based rate control remains largely underexplored for range image compression (RIC) under dynamic bandwidth conditions. To address these limitations, we propose D-Compress, a new detail-preserving and fast RIC framework tailored for real-time streaming. D-Compress integrates both intra- and inter-frame prediction with an adaptive discrete wavelet transform approach for precise residual compression. Additionally, we introduce a new RDO-based rate control algorithm for RIC through new rate-distortion modeling. Extensive evaluations on various datasets demonstrate the superiority of D-Compress, which outperforms state-of-the-art (SOTA) compression methods in both geometric accuracy and downstream task performance, particularly at compression ratios exceeding 100x, while maintaining real-time execution on resource-constrained hardware. Moreover, evaluations under dynamic bandwidth conditions validate the robustness of its rate control mechanism.
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URL
https://arxiv.org/abs/2603.13699
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DiT-IC: Aligned Diffusion Transformer for Efficient Image Compression
2026-03-13 16:56:00Junqi Shi, Ming Lu, Xingchen Li, Anle Ke, Ruiqi Zhang, Zhan MaAbstract
Diffusion-based image compression has recently shown outstanding perceptual fidelity, yet its practicality is hindered by prohibitive sampling overhead and high memory usage. Most existing diffusion codecs employ U-Net architectures, where hierarchical downsampling forces diffusion to operate in shallow latent spaces (typically with only 8x spatial downscaling), resulting in excessive computation. In contrast, conventional VAE-based codecs work in much deeper latent domains (16x - 64x downscaled), motivating a key question: Can diffusion operate effectively in such compact latent spaces without compromising reconstruction quality? To address this, we introduce DiT-IC, an Aligned Diffusion Transformer for Image Compression, which replaces the U-Net with a Diffusion Transformer capable of performing diffusion in latent space entirely at 32x downscaled resolution. DiT-IC adapts a pretrained text-to-image multi-step DiT into a single-step reconstruction model through three key alignment mechanisms: (1) a variance-guided reconstruction flow that adapts denoising strength to latent uncertainty for efficient reconstruction; (2) a self-distillation alignment that enforces consistency with encoder-defined latent geometry to enable one-step diffusion; and (3) a latent-conditioned guidance that replaces text prompts with semantically aligned latent conditions, enabling text-free inference. With these designs, DiT-IC achieves state-of-the-art perceptual quality while offering up to 30x faster decoding and drastically lower memory usage than existing diffusion-based codecs. Remarkably, it can reconstruct 2048x2048 images on a 16 GB laptop GPU.
Abstract (translated)
URL
https://arxiv.org/abs/2603.13162
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An FPGA Implementation of Displacement Vector Search for Intra Pattern Copy in JPEG XS
2026-03-11 11:37:50Qiyue Chen, Yao Li, Jie Tao, Song Chen, Li Li, Dong LiuAbstract
Recently, progress has been made on the Intra Pattern Copy (IPC) tool for JPEG XS, an image compression standard designed for low-latency and low-complexity coding. IPC performs wavelet-domain intra compensation predictions to reduce spatial redundancy in screen content. A key module of IPC is the displacement vector (DV) search, which aims to solve the optimal prediction reference offset. However, the DV search process is computationally intensive, posing challenges for practical hardware deployment. In this paper, we propose an efficient pipelined FPGA architecture design for the DV search module to promote the practical deployment of IPC. Optimized memory organization, which leverages the IPC computational characteristics and data inherent reuse patterns, is further introduced to enhance the performance. Experimental results show that our proposed architecture achieves a throughput of 38.3 Mpixels/s with a power consumption of 277 mW, demonstrating its feasibility for practical hardware implementation in IPC and other predictive coding tools, and providing a promising foundation for ASIC deployment.
Abstract (translated)
最近，在JPEG XS的Intra Pattern Copy（IPC）工具方面取得了进展。JPEG XS是一种专为低延迟和低复杂度编码设计的图像压缩标准，而IPC通过执行小波域内的帧内补偿预测来减少屏幕内容中的空间冗余。IPC的一个关键模块是位移向量(DV)搜索，其目标是解决最优预测参考偏移问题。然而，DV搜索过程计算密集型高，给实际硬件部署带来了挑战。在本文中，我们提出了一种高效的流水线FPGA架构设计，用于优化DV搜索模块，以促进IPC的实际部署。通过利用IPC的计算特性以及数据内在重用模式进行了内存组织优化，进一步增强了性能。实验结果表明，所提出的架构实现了38.3 Mpixels/s的吞吐量和277 mW的功耗，在IPC及其他预测编码工具的实际硬件实现中证明了其可行性，并为ASIC部署提供了有前景的基础。
URL
https://arxiv.org/abs/2603.10671
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ARCHE: Autoregressive Residual Compression with Hyperprior and Excitation
2026-03-10 19:32:10Sofia Iliopoulou, Dimitris Ampeliotis, Athanassios SkodrasAbstract
Recent progress in learning-based image compression has demonstrated that end-to-end optimization can substantially outperform traditional codecs by jointly learning compact latent representations and probabilistic entropy models. However, many existing approaches achieve high rate-distortion efficiency at the expense of increased computational cost and limited parallelism. This paper presents ARCHE - Autoregressive Residual Compression with Hyperprior and Excitation, an end-to-end learned image compression framework that balances modeling accuracy and computational efficiency. The proposed architecture unifies hierarchical, spatial, and channel-based priors within a single probabilistic framework, capturing both global and local dependencies in the latent representation of the image, while employing adaptive feature recalibration and residual refinement to enhance latent representation quality. Without relying on recurrent or transformer-based components, ARCHE attains state-of-the-art rate-distortion efficiency: it reduces the BD-Rate by approximately 48% relative to the commonly used benchmark model of Balle et al., 30% relative to the channel-wise autoregressive model of Minnen & Singh and 5% against the VVC Intra codec on the Kodak benchmark dataset. The framework maintains computational efficiency with 95M parameters and 222ms running time per image. Visual comparisons confirm sharper textures and improved color fidelity, particularly at lower bit rates, demonstrating that accurate entropy modeling can be achieved through efficient convolutional designs suitable for practical deployment.
Abstract (translated)
最近基于学习的图像压缩研究显示，端到端优化可以通过联合学习紧凑的潜在表示和概率熵模型，在很大程度上超越传统编解码器。然而，许多现有的方法通过增加计算成本和有限的并行性来实现高率失真效率。本文介绍了一种名为ARCHE（自回归残差压缩与超前量和激励）的端到端学习图像压缩框架，该框架平衡了建模精度和计算效率。所提出的架构将层次化、空间性和通道级别的先验统一在一个概率框架内，捕捉到了图像潜在表示中的全局和局部依赖关系，并通过采用自适应特征再校准和残差细化来提高潜在表示的质量。在不依赖递归或基于变换器的组件的情况下，ARCHE达到了最先进的率失真效率：它将相对于Balle等人常用的基准模型减少了约48%的BD-Rate（比特率差异），比Minnen & Singh提出的通道级别的自回归模型减少了30%，以及比VVC Intra编解码器在Kodak基准数据集上减少了5%。该框架以95M参数和每张图片222ms的运行时间保持计算效率。视觉比较确认了纹理更清晰、颜色保真度更高的优势，特别是在较低比特率下，证明通过适合实际部署的有效卷积设计可以实现准确的概率模型化。
URL
https://arxiv.org/abs/2603.10188
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Robust Provably Secure Image Steganography via Latent Iterative Optimization
2026-03-10 08:29:49Yanan Li, Zixuan Wang, Qiyang Xiao, Yanzhen RenAbstract
We propose a robust and provably secure image steganography framework based on latent-space iterative optimization. Within this framework, the receiver treats the transmitted image as a fixed reference and iteratively refines a latent variable to minimize the reconstruction error, thereby improving message extraction accuracy. Unlike prior methods, our approach preserves the provable security of the embedding while markedly enhancing robustness under various compression and image processing scenarios. On benchmark datasets, the experimental results demonstrate that the proposed iterative optimization not only improves robustness against image compression while preserving provable security, but can also be applied as an independent module to further reinforce robustness in other provably secure steganographic schemes. This highlights the practicality and promise of latent-space optimization for building reliable, robust, and secure steganographic systems.
Abstract (translated)
我们提出了一种基于潜在空间迭代优化的稳健且可证明安全的图像隐写框架。在此框架下，接收方将传输的图像视为固定参考，并通过迭代改进一个潜在变量来最小化重构误差，从而提高消息提取的准确性。与以往的方法不同，我们的方法在保持嵌入可证明安全性的同时，在各种压缩和图像处理场景中显著增强了鲁棒性。 在基准数据集上的实验结果表明，所提出的迭代优化不仅能够在保证可证明安全性的前提下增强对图像压缩的抗干扰能力，还可以作为一个独立模块应用于其他可证明安全的隐写方案中以进一步增强其鲁棒性。这突显了潜在空间优化在构建可靠、稳健且安全的隐写系统中的实用性和前景。
URL
https://arxiv.org/abs/2603.09348
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HiDE: Hierarchical Dictionary-Based Entropy Modeling for Learned Image Compression
2026-03-06 15:15:07Haoxuan Xiong, Yuanyuan Xu, Kun Zhu, Yiming Wang, Baoliu YeAbstract
Learned image compression (LIC) has achieved remarkable coding efficiency, where entropy modeling plays a pivotal role in minimizing bitrate through informative priors. Existing methods predominantly exploit internal contexts within the input image, yet the rich external priors embedded in large-scale training data remain largely underutilized. Recent advances in dictionary-based entropy models have demonstrated that incorporating external priors can substantially enhance compression performance. However, current approaches organize heterogeneous external priors within a single-level dictionary, resulting in imbalanced utilization and limited representational capacity. Moreover, effective entropy modeling requires not only expressive priors but also a parameter estimation network capable of interpreting them. To address these challenges, we propose HiDE, a Hierarchical Dictionary-based Entropy modeling framework for learned image compression. HiDE decomposes external priors into global structural and local detail dictionaries with cascaded retrieval, enabling structured and efficient utilization of external information. Moreover, a context-aware parameter estimator with parallel multi-receptive-field design is introduced to adaptively exploit heterogeneous contexts for accurate conditional probability estimation. Experimental results show that HiDE achieves 18.5%, 21.99%, and 24.01% BD-rate savings over VTM-12.1 on the Kodak, CLIC, and Tecnick datasets, respectively.
Abstract (translated)
学习图像压缩（LIC）已经实现了显著的编码效率，其中熵建模通过提供信息先验来减少比特率起着至关重要的作用。现有方法主要利用输入图像内部上下文，然而大规模训练数据中嵌入的丰富外部先验却大多未被充分利用。最近基于字典的熵模型的发展表明，整合外部先验可以显著提高压缩性能。但是，目前的方法将异构外部先验组织在一个级别的字典中，导致了不平衡的利用和有限的表现能力。此外，有效的熵建模不仅需要表现力强的先验，还需要能够解释这些先验的参数估计网络。 为了应对上述挑战，我们提出了HiDE框架，即基于层次字典的熵模型化框架用于学习图像压缩。HiDE将外部先验分解为全局结构和局部细节字典，并通过级联检索进行利用，从而实现对外部信息有条理且高效的使用。此外，该方法引入了一个具有并行多感受野设计的上下文感知参数估计器，以适应性地利用异构上下文来准确估算条件概率。 实验结果表明，在Kodak、CLIC和Tecnick数据集上，HiDE相对于VTM-12.1分别实现了18.5%、21.99%和24.01%的BD率节省。
URL
https://arxiv.org/abs/2603.06766
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TextBoost: Boosting Scene Text Fidelity in Ultra-low Bitrate Image Compression
2026-03-04 14:35:10Bingxin Wang, Yuan Lan, Zhaoyi Sun, Yang Xiang, Jie SunAbstract
Ultra-low bitrate image compression faces a critical challenge: preserving small-font scene text while maintaining overall visual quality. Region-of-interest (ROI) bit allocation can prioritize text but often degrades global fidelity, leading to a trade-off between local accuracy and overall image quality. Instead of relying on ROI coding, we incorporate auxiliary textual information extracted by OCR and transmitted with negligible overhead, enabling the decoder to leverage this semantic guidance. Our method, TextBoost, operationalizes this idea through three strategic designs: (i) adaptively filtering OCR outputs and rendering them into a guidance map; (ii) integrating this guidance with decoder features in a calibrated manner via an attention-guided fusion block; and (iii) enforcing guidance-consistent reconstruction in text regions with a regularizing loss that promotes natural blending with the scene. Extensive experiments on TextOCR and ICDAR 2015 demonstrate that TextBoost yields up to 60.6% higher text-recognition F1 at comparable Peak Signal-to-Noise Ratio (PSNR) and bits per pixel (bpp), producing sharper small-font text while preserving global image quality and effectively decoupling text enhancement from global rate-distortion optimization.
Abstract (translated)
超低比特率图像压缩面临的一个关键挑战是，在保持整体视觉质量的同时，保留小字体场景文本。区域感兴趣（ROI）的比特分配可以优先处理文本，但这通常会降低全局保真度，导致局部准确性和整体图像质量之间的权衡。我们不依赖于ROI编码，而是结合由OCR提取并以几乎无开销的方式传输的辅助文本信息，使解码器能够利用这种语义指导。 我们的方法TextBoost通过三个战略设计将这一理念付诸实践：(i) 自适应地过滤OCR输出，并将其转换为引导图；(ii) 通过注意力引导融合块以校准方式整合该引导与解码器特征；(iii) 在文本区域强制执行一致性重建，同时采用正则化损失来促进自然的场景融合。在TextOCR和ICDAR 2015数据集上的广泛实验表明，相较于同等峰值信噪比（PSNR）和每像素比特数（bpp），TextBoost可以将文本识别F1值提高高达60.6%，同时生成更清晰的小字体文本，并保持全局图像质量，有效地解耦了文本增强与全局率失真优化之间的关系。
URL
https://arxiv.org/abs/2603.04115
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Parallax to Align Them All: An OmniParallax Attention Mechanism for Distributed Multi-View Image Compression
2026-03-04 01:00:23Haotian Zhang, Feiyue Long, Yixin Yu, Jian Xue, Haocheng Tang, Tongda Xu, Zhenning Shi, Yan Wang, Siwei Ma, Jiaqi ZhangAbstract
Multi-view image compression (MIC) aims to achieve high compression efficiency by exploiting inter-image correlations, playing a crucial role in 3D applications. As a subfield of MIC, distributed multi-view image compression (DMIC) offers performance comparable to MIC while eliminating the need for inter-view information at the encoder side. However, existing methods in DMIC typically treat all images equally, overlooking the varying degrees of correlation between different views during decoding, which leads to suboptimal coding performance. To address this limitation, we propose a novel $\textbf{OmniParallax Attention Mechanism}$ (OPAM), which is a general mechanism for explicitly modeling correlations and aligned features between arbitrary pairs of information sources. Building upon OPAM, we propose a Parallax Multi Information Fusion Module (PMIFM) to adaptively integrate information from different sources. PMIFM is incorporated into both the joint decoder and the entropy model to construct our end-to-end DMIC framework, $\textbf{ParaHydra}$. Extensive experiments demonstrate that $\textbf{ParaHydra}$ is $\textbf{the first DMIC method}$ to significantly surpass state-of-the-art MIC codecs, while maintaining low computational overhead. Performance gains become more pronounced as the number of input views increases. Compared with LDMIC, $\textbf{ParaHydra}$ achieves bitrate savings of $\textbf{19.72%}$ on WildTrack(3) and up to $\textbf{24.18%}$ on WildTrack(6), while significantly improving coding efficiency (as much as $\textbf{65}\times$ in decoding and $\textbf{34}\times$ in encoding).
Abstract (translated)
多视角图像压缩（MIC）旨在通过利用图像间的相关性来实现高效压缩，在三维应用中扮演着重要角色。作为MIC的一个子领域，分布式多视角图像压缩（DMIC）在性能上可与MIC相媲美，同时消除了编码端对视图间信息的需求。然而，现有的DMIC方法通常认为所有图像同等重要，忽略了在解码过程中不同视图之间相关性的差异性，导致了次优的编码效果。 为解决这一限制，我们提出了一种新颖的**全方位偏移注意力机制（OmniParallax Attention Mechanism, OPAM）**，这是一种通用机制，用于明确建模任意信息源对之间的相关性和对齐特征。基于OPAM，我们提出了一个**视差多信息融合模块（PMIFM）**，以自适应地整合来自不同来源的信息。我们将PMIFM集成到联合解码器和熵模型中，构建了我们的端到端DMIC框架——**ParaHydra**。 广泛的实验表明，**ParaHydra**是第一个显著超越最新MIC编解码器性能的DMIC方法，并且保持了较低的计算开销。随着输入视图数量的增加，性能提升更加明显。与LDMIC相比，在WildTrack(3)上，**ParaHydra**实现了19.72%的比特率节省，在WildTrack(6)上最多可达到24.18%，同时大幅提高了编码效率（解码速度最高提升了65倍，编码速度提升高达34倍）。
URL
https://arxiv.org/abs/2603.03615
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ProGIC: Progressive and Lightweight Generative Image Compression with Residual Vector Quantization
2026-03-03 11:47:05Hao Cao, Chengbin Liang, Wenqi Guo, Zhijin Qin, Jungong HanAbstract
Recent advances in generative image compression (GIC) have delivered remarkable improvements in perceptual quality. However, many GICs rely on large-scale and rigid models, which severely constrain their utility for flexible transmission and practical deployment in low-bitrate scenarios. To address these issues, we propose Progressive Generative Image Compression (ProGIC), a compact codec built on residual vector quantization (RVQ). In RVQ, a sequence of vector quantizers encodes the residuals stage by stage, each with its own codebook. The resulting codewords sum to a coarse-to-fine reconstruction and a progressive bitstream, enabling previews from partial data. We pair this with a lightweight backbone based on depthwise-separable convolutions and small attention blocks, enabling practical deployment on both GPUs and CPU-only devices. Experimental results show that ProGIC attains comparable compression performance compared with previous methods. It achieves bitrate savings of up to 57.57% on DISTS and 58.83% on LPIPS compared to MS-ILLM on the Kodak dataset. Beyond perceptual quality, ProGIC enables progressive transmission for flexibility, and also delivers over 10 times faster encoding and decoding compared with MS-ILLM on GPUs for efficiency.
Abstract (translated)
最近在生成图像压缩（GIC）领域取得的进展显著提升了感知质量。然而，许多现有的GIC方法依赖于大规模且刚性的模型架构，这严重限制了它们在网络传输中的灵活性和在低比特率场景下的实际部署能力。为了解决这些问题，我们提出了渐进式生成图像压缩（ProGIC），这是一种基于残差向量量化（RVQ）构建的紧凑型编解码器。 在RVQ中，一系列向量量化器依次编码残差数据，每个量化器都有自己的代码本。这种机制产生了从粗糙到精细的重建结果和渐进式的比特流，从而允许使用部分数据进行预览。我们还采用了一种基于可分离深度卷积和小注意力模块构建的轻量级骨干网络，这使得ProGIC能够在支持GPU设备的同时，也能在仅CPU设备上实际部署。 实验结果显示，与先前的方法相比，ProGIC实现了相仿的压缩性能。具体而言，在Kodak数据集上的DISTS指标下，它比MS-ILLM方法节省了高达57.57%的比特率；而在LPIPS指标下，则节省了58.83%。此外，除了感知质量之外，ProGIC还支持渐进式传输以增强灵活性，并且在GPU上的编码和解码速度上比MS-ILLM快超过10倍，从而大大提高了效率。
URL
https://arxiv.org/abs/2603.02897
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CADC: Content Adaptive Diffusion-Based Generative Image Compression
2026-02-25 05:35:45Xihua Sheng, Lingyu Zhu, Tianyu Zhang, Dong Liu, Shiqi Wang, Jing WangAbstract
Diffusion-based generative image compression has demonstrated remarkable potential for achieving realistic reconstruction at ultra-low bitrates. The key to unlocking this potential lies in making the entire compression process content-adaptive, ensuring that the encoder's representation and the decoder's generative prior are dynamically aligned with the semantic and structural characteristics of the input image. However, existing methods suffer from three critical limitations that prevent effective content adaptation. First, isotropic quantization applies a uniform quantization step, failing to adapt to the spatially varying complexity of image content and creating a misalignment with the diffusion model's noise-dependent prior. Second, the information concentration bottleneck -- arising from the dimensional mismatch between the high-dimensional noisy latent and the diffusion decoder's fixed input -- prevents the model from adaptively preserving essential semantic information in the primary channels. Third, existing textual conditioning strategies either need significant textual bitrate overhead or rely on generic, content-agnostic textual prompts, thereby failing to provide adaptive semantic guidance efficiently. To overcome these limitations, we propose a content-adaptive diffusion-based image codec with three technical innovations: 1) an Uncertainty-Guided Adaptive Quantization method that learns spatial uncertainty maps to adaptively align quantization distortion with content characteristics; 2) an Auxiliary Decoder-Guided Information Concentration method that uses a lightweight auxiliary decoder to enforce content-aware information preservation in the primary latent channels; and 3) a Bitrate-Free Adaptive Textual Conditioning method that derives content-aware textual descriptions from the auxiliary reconstructed image, enabling semantic guidance without bitrate cost.
Abstract (translated)
基于扩散模型的生成图像压缩技术在超低比特率下实现逼真重建方面展现了巨大的潜力。要充分发挥这种潜力，关键在于使整个压缩过程具备内容自适应性，确保编码器表示与解码器先验能够根据输入图像的语义和结构特性动态调整。然而，现有的方法存在三个主要限制，这些限制阻碍了有效的内容适应： 1. **同向量化**：现有方法采用统一的量化步长，未能针对图像内容的空间复杂度变化进行调整，导致与扩散模型噪声依赖先验之间产生不匹配。 2. **信息集中瓶颈**：高维噪声潜在空间和固定输入尺寸的解码器之间的维度不匹配问题，限制了模型在主要通道中自适应地保留关键语义信息的能力。 3. **文本条件策略不足**：现有的文本引导策略要么需要较高的文本比特率开销，要么依赖于通用且无特定内容感知性的提示词，因此无法有效提供适应性语义指导。 为克服这些限制，我们提出了一种基于扩散模型的图像编码解码器框架，并引入了以下三项技术创新： 1. **不确定性导向自适应量化**：通过学习空间不确定度图来动态调整量化失真与内容特性之间的匹配程度。 2. **辅助解码器引导信息集中方法**：利用一个轻量级的辅助解码器，强制在主要潜在通道中保持以内容为中心的信息保留。 3. **无比特率成本自适应文本条件策略**：从辅助重建图像中提取出内容感知性的描述性文本，从而能够在不增加比特率的情况下提供有效的语义指导。
URL
https://arxiv.org/abs/2602.21591
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BiRQA: Bidirectional Robust Quality Assessment for Images
2026-02-23 20:52:56Aleksandr Gushchin, Dmitriy S. Vatolin, Anastasia AntsiferovaAbstract
Full-Reference image quality assessment (FR IQA) is important for image compression, restoration and generative modeling, yet current neural metrics remain slow and vulnerable to adversarial perturbations. We present BiRQA, a compact FR IQA metric model that processes four fast complementary features within a bidirectional multiscale pyramid. A bottom-up attention module injects fine-scale cues into coarse levels through an uncertainty-aware gate, while a top-down cross-gating block routes semantic context back to high resolution. To enhance robustness, we introduce Anchored Adversarial Training, a theoretically grounded strategy that uses clean "anchor" samples and a ranking loss to bound pointwise prediction error under attacks. On five public FR IQA benchmarks BiRQA outperforms or matches the previous state of the art (SOTA) while running ~3x faster than previous SOTA models. Under unseen white-box attacks it lifts SROCC from 0.30-0.57 to 0.60-0.84 on KADID-10k, demonstrating substantial robustness gains. To our knowledge, BiRQA is the only FR IQA model combining competitive accuracy with real-time throughput and strong adversarial resilience.
Abstract (translated)
全参考图像质量评估（FR IQA）在图像压缩、恢复和生成模型中非常重要，然而目前的神经网络度量方法仍然速度慢且易受对抗性干扰。我们提出了一种名为BiRQA的小巧的全参考图像质量评估模型，它在一个双向多尺度金字塔内处理四种快速互补特征。通过一个不确定性感知门，自下而上的注意力模块将细粒度线索注入到粗粒度级别，而自上而下的交叉门控块则将语义上下文传递回高分辨率区域。为了增强其鲁棒性，我们引入了基于理论依据的锚定对抗训练策略，该策略利用干净的“基准”样本和排名损失来限制在攻击情况下逐点预测误差。在五个公开的全参考图像质量评估基准测试中，BiRQA的表现超越或匹配之前的最先进模型（SOTA），同时运行速度比之前的SOTA模型快约3倍。在未见过的白盒攻击下，BiRQA将KADID-10k数据集上的SROCC值从0.30至0.57提高到了0.60至0.84之间，展示了显著的对抗性鲁棒性提升。据我们所知，BiRQA是第一个结合了高精度、实时吞吐量和强大抗干扰能力的全参考图像质量评估模型。
URL
https://arxiv.org/abs/2602.20351
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SEG-JPEG: Simple Visual Semantic Communications for Remote Operation of Automated Vehicles over Unreliable Wireless Networks
2026-02-16 23:28:10Sebastian Donnelly, Ruth Anderson, George Economides, James Broughton, Peter Ball, Alexander Rast, Andrew BradleyAbstract
Remote Operation is touted as being key to the rapid deployment of automated vehicles. Streaming imagery to control connected vehicles remotely currently requires a reliable, high throughput network connection, which can be limited in real-world remote operation deployments relying on public network infrastructure. This paper investigates how the application of computer vision assisted semantic communication can be used to circumvent data loss and corruption associated with traditional image compression techniques. By encoding the segmentations of detected road users into colour coded highlights within low resolution greyscale imagery, the required data rate can be reduced by 50 \% compared with conventional techniques, while maintaining visual clarity. This enables a median glass-to-glass latency of below 200ms even when the network data rate is below 500kbit/s, while clearly outlining salient road users to enhance situational awareness of the remote operator. The approach is demonstrated in an area of variable 4G mobile connectivity using an automated last-mile delivery vehicle. With this technique, the results indicate that large-scale deployment of remotely operated automated vehicles could be possible even on the often constrained public 4G/5G mobile network, providing the potential to expedite the nationwide roll-out of automated vehicles.
Abstract (translated)
URL
https://arxiv.org/abs/2602.15258
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Dual-Representation Image Compression at Ultra-Low Bitrates via Explicit Semantics and Implicit Textures
2026-02-05 02:14:05Chuqin Zhou, Xiaoyue Ling, Yunuo Chen, Jincheng Dai, Guo Lu, Wenjun ZhangAbstract
While recent neural codecs achieve strong performance at low bitrates when optimized for perceptual quality, their effectiveness deteriorates significantly under ultra-low bitrate conditions. To mitigate this, generative compression methods leveraging semantic priors from pretrained models have emerged as a promising paradigm. However, existing approaches are fundamentally constrained by a tradeoff between semantic faithfulness and perceptual realism. Methods based on explicit representations preserve content structure but often lack fine-grained textures, whereas implicit methods can synthesize visually plausible details at the cost of semantic drift. In this work, we propose a unified framework that bridges this gap by coherently integrating explicit and implicit representations in a training-free manner. Specifically, We condition a diffusion model on explicit high-level semantics while employing reverse-channel coding to implicitly convey fine-grained details. Moreover, we introduce a plug-in encoder that enables flexible control of the distortion-perception tradeoff by modulating the implicit information. Extensive experiments demonstrate that the proposed framework achieves state-of-the-art rate-perception performance, outperforming existing methods and surpassing DiffC by 29.92%, 19.33%, and 20.89% in DISTS BD-Rate on the Kodak, DIV2K, and CLIC2020 datasets, respectively.
Abstract (translated)
虽然近期的神经编码器在优化感知质量时，在低比特率条件下表现出色，但在极低比特率条件下的效果显著下降。为了缓解这一问题，生成式压缩方法利用预训练模型中的语义先验，作为一种有前景的方法出现。然而，现有方法从根本上受到语义忠实性和感知逼真性之间的权衡制约：基于显式表示的方法可以保持内容结构，但往往缺乏精细纹理；而隐式方法虽然能够合成视觉上合理的细节，但却可能以牺牲语义一致性为代价。 在本工作中，我们提出了一种统一框架，通过训练自由的方式将显式和隐式表示一致地整合起来，从而弥合了这一差距。具体而言，我们在扩散模型中使用显式的高层语义进行条件化设置，并采用反向通道编码来隐式传达精细细节。此外，我们引入了一个插件式编码器，可以通过调节隐式信息灵活控制失真-感知权衡。 广泛的实验表明，所提出的框架在率-感知性能方面达到了最先进的水平，优于现有的方法，在Kodak、DIV2K和CLIC2020数据集上分别比DiffC高出29.92%、19.33% 和 20.89%的DISTS BD-Rate。
URL
https://arxiv.org/abs/2602.05213
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One-Step Diffusion for Perceptual Image Compression
2026-02-02 03:04:08Yiwen Jia, Hao Wei, Yanhui Zhou, Chenyang GeAbstract
Diffusion-based image compression methods have achieved notable progress, delivering high perceptual quality at low bitrates. However, their practical deployment is hindered by significant inference latency and heavy computational overhead, primarily due to the large number of denoising steps required during decoding. To address this problem, we propose a diffusion-based image compression method that requires only a single-step diffusion process, significantly improving inference speed. To enhance the perceptual quality of reconstructed images, we introduce a discriminator that operates on compact feature representations instead of raw pixels, leveraging the fact that features better capture high-level texture and structural details. Experimental results show that our method delivers comparable compression performance while offering a 46$\times$ faster inference speed compared to recent diffusion-based approaches. The source code and models are available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2602.01570
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REMAC: Reference-Based Martian Asymmetrical Image Compression
2026-01-26 14:55:17Qing Ding, Mai Xu, Shengxi Li, Xin Deng, Xin ZouAbstract
To expedite space exploration on Mars, it is indispensable to develop an efficient Martian image compression method for transmitting images through the constrained Mars-to-Earth communication channel. Although the existing learned compression methods have achieved promising results for natural images from earth, there remain two critical issues that hinder their effectiveness for Martian image compression: 1) They overlook the highly-limited computational resources on Mars; 2) They do not utilize the strong \textit{inter-image} similarities across Martian images to advance image compression performance. Motivated by our empirical analysis of the strong \textit{intra-} and \textit{inter-image} similarities from the perspective of texture, color, and semantics, we propose a reference-based Martian asymmetrical image compression (REMAC) approach, which shifts computational complexity from the encoder to the resource-rich decoder and simultaneously improves compression performance. To leverage \textit{inter-image} similarities, we propose a reference-guided entropy module and a ref-decoder that utilize useful information from reference images, reducing redundant operations at the encoder and achieving superior compression performance. To exploit \textit{intra-image} similarities, the ref-decoder adopts a deep, multi-scale architecture with enlarged receptive field size to model long-range spatial dependencies. Additionally, we develop a latent feature recycling mechanism to further alleviate the extreme computational constraints on Mars. Experimental results show that REMAC reduces encoder complexity by 43.51\% compared to the state-of-the-art method, while achieving a BD-PSNR gain of 0.2664 dB.
Abstract (translated)
URL
https://arxiv.org/abs/2601.18547
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Lossy Image Compression -- A Frequent Sequence Mining perspective employing efficient Clustering
2026-01-24 20:44:55Avinash Kadimisetty, Oswald C, Sivaselvan B, Alekhya KadimisettyAbstract
This work explores the scope of Frequent Sequence Mining in the domain of Lossy Image Compression. The proposed work is based on the idea of clustering pixels and using the cluster identifiers in the compression. The DCT phase in JPEG is replaced with a combination of closed frequent sequence mining and k-means clustering to handle the redundant data effectively. This method focuses mainly on applying k-means clustering in parallel to all blocks of each component of the image to reduce the compression time. Conventional GSP algorithm is refined to optimize the cardinality of patterns through a novel pruning strategy, thus achieving a good reduction in the code table size. Simulations of the proposed algorithm indicate significant gains in compression ratio and quality in relation to the existing alternatives.
Abstract (translated)
URL
https://arxiv.org/abs/2601.18821
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Frequent Pattern Mining approach to Image Compression
2026-01-24 20:32:16Avinash Kadimisetty, C. Oswald, B. SivalselvanAbstract
The paper focuses on Image Compression, explaining efficient approaches based on Frequent Pattern Mining(FPM). The proposed compression mechanism is based on clustering similar pixels in the image and thus using cluster identifiers in image compression. Redundant data in the image is effectively handled by replacing the DCT phase of conventional JPEG through a mixture of k-means Clustering and Closed Frequent Sequence Mining. To optimize the cardinality of pattern(s) in encoding, efficient pruning techniques have been used through the refinement of Conventional Generalized Sequential Pattern Mining(GSP) algorithm. We have proposed a mechanism for finding the frequency of a sequence which will yield significant reduction in the code table size. The algorithm is tested by compressing benchmark datasets yielding an improvement of 45% in compression ratios, often outperforming the existing alternatives. PSNR and SSIM, which are the image quality metrics, have been tested which show a negligible loss in visual quality.
Abstract (translated)
URL
https://arxiv.org/abs/2602.00100
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Saliency Driven Imagery Preprocessing for Efficient Compression -- Industrial Paper
2026-01-24 18:52:19Justin Downes, Sam Saltwick, Anthony ChenAbstract
The compression of satellite imagery remains an important research area as hundreds of terabytes of images are collected every day, which drives up storage and bandwidth costs. Although progress has been made in increasing the resolution of these satellite images, many downstream tasks are only interested in small regions of any given image. These areas of interest vary by task but, once known, can be used to optimize how information within the image is encoded. Whereas standard image encoding methods, even those optimized for remote sensing, work on the whole image equally, there are emerging methods that can be guided by saliency maps to focus on important areas. In this work we show how imagery preprocessing techniques driven by saliency maps can be used with traditional lossy compression coding standards to create variable rate image compression within a single large satellite image. Specifically, we use variable sized smoothing kernels that map to different quantized saliency levels to process imagery pixels in order to optimize downstream compression and encoding schemes.
Abstract (translated)
URL
https://arxiv.org/abs/2601.17555
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LL-GaussianImage: Efficient Image Representation for Zero-shot Low-Light Enhancement with 2D Gaussian Splatting
2026-01-22 09:01:08Yuhan Chen, Wenxuan Yu, Guofa Li, Yijun Xu, Ying Fang, Yicui Shi, Long Cao, Wenbo Chu, Keqiang LiAbstract
2D Gaussian Splatting (2DGS) is an emerging explicit scene representation method with significant potential for image compression due to high fidelity and high compression ratios. However, existing low-light enhancement algorithms operate predominantly within the pixel domain. Processing 2DGS-compressed images necessitates a cumbersome decompression-enhancement-recompression pipeline, which compromises efficiency and introduces secondary degradation. To address these limitations, we propose LL-GaussianImage, the first zero-shot unsupervised framework designed for low-light enhancement directly within the 2DGS compressed representation domain. Three primary advantages are offered by this framework. First, a semantic-guided Mixture-of-Experts enhancement framework is designed. Dynamic adaptive transformations are applied to the sparse attribute space of 2DGS using rendered images as guidance to enable compression-as-enhancement without full decompression to a pixel grid. Second, a multi-objective collaborative loss function system is established to strictly constrain smoothness and fidelity during enhancement, suppressing artifacts while improving visual quality. Third, a two-stage optimization process is utilized to achieve reconstruction-as-enhancement. The accuracy of the base representation is ensured through single-scale reconstruction and network robustness is enhanced. High-quality enhancement of low-light images is achieved while high compression ratios are maintained. The feasibility and superiority of the paradigm for direct processing within the compressed representation domain are validated through experimental results.
Abstract (translated)
2D高斯点阵（2DGS）是一种新兴的显式场景表示方法，由于其高保真度和高压缩比，在图像压缩方面具有巨大的潜力。然而，现有的低光增强算法主要在像素域内操作。处理通过2DGS压缩的图像需要一个复杂的解压-增强-重新压缩管道流程，这不仅效率低下，还会引入二次退化问题。为了克服这些限制，我们提出了LL-GaussianImage框架，这是首个针对低光图像直接在2DGS压缩表示域内进行无监督零样本增强处理的方法。 该框架提供了三个主要优势： 1. 设计了一个基于语义引导的专家混合增强框架。通过使用渲染图像作为指导，在不完全解压到像素网格的情况下对2DGS的稀疏属性空间应用动态自适应变换，从而实现压缩与增强一体化的效果。 2. 建立了一种多目标协作损失函数系统，严格限制了在增强过程中保持平滑度和保真度的要求。这种方法不仅可以抑制伪影，还可以提高视觉质量。 3. 采用两阶段优化过程来实现重建即增强的目标。通过单尺度重建确保基础表示的准确性，并加强网络鲁棒性，在维持高压缩比的同时实现了低光图像的高质量增强效果。 实验结果验证了直接在压缩表示域内进行处理的可行性和优越性，展示了LL-GaussianImage框架的有效性。
URL
https://arxiv.org/abs/2601.15772
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