SEEC: Segmentation-Assisted Multi-Entropy Models for Learned Lossless Image Compression

• URL: http://arxiv.org/abs/2509.07704v1
• Date: Tue, 09 Sep 2025 13:10:11 GMT
• Title: SEEC: Segmentation-Assisted Multi-Entropy Models for Learned Lossless Image Compression
• Authors: Chunhang Zheng, Zichang Ren, Dou Li,
• Abstract summary: We propose Multi-Assisted Multi-Entropy Models for Lossless Image Compression (SEEC)<n>Our framework utilizes semantic segmentation to guide the selection and adaptation of multiple entropy models.<n>SEEC achieves state-of-the-art compression ratios while introducing only minimal encoding and decoding latency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, learned image compression has attracted considerable attention due to its superior performance over traditional methods. However, most existing approaches employ a single entropy model to estimate the probability distribution of pixel values across the entire image, which limits their ability to capture the diverse statistical characteristics of different semantic regions. To overcome this limitation, we propose Segmentation-Assisted Multi-Entropy Models for Lossless Image Compression (SEEC). Our framework utilizes semantic segmentation to guide the selection and adaptation of multiple entropy models, enabling more accurate probability distribution estimation for distinct semantic regions. Specifically, SEEC first extracts image features and then applies semantic segmentation to identify different regions, each assigned a specialized entropy model to better capture its unique statistical properties. Finally, a multi-channel discrete logistic mixture likelihood is employed to model the pixel value distributions effectively. Experimental results on benchmark datasets demonstrate that SEEC achieves state-of-the-art compression ratios while introducing only minimal encoding and decoding latency. With superior performance, the proposed model also supports Regions of Interest (ROIs) coding condition on the provided segmentation mask. Our code is available at https://github.com/chunbaobao/SEEC.

Related papers

• A Simple Approach to Unifying Diffusion-based Conditional Generation [63.389616350290595]
  We introduce a simple, unified framework to handle diverse conditional generation tasks.<n>Our approach enables versatile capabilities via different inference-time sampling schemes.<n>Our model supports additional capabilities like non-spatially aligned and coarse conditioning.
  arXiv  Detail & Related papers  (2024-10-15T09:41:43Z)
• CriDiff: Criss-cross Injection Diffusion Framework via Generative Pre-train for Prostate Segmentation [60.61972883059688]
  CriDiff is a two-stage feature injecting framework with a Crisscross Injection Strategy (CIS) and a Generative Pre-train (GP) approach for prostate segmentation. To effectively learn multi-level of edge features and non-edge features, we proposed two parallel conditioners in the CIS. The GP approach eases the inconsistency between the images features and the diffusion model without adding additional parameters.
  arXiv  Detail & Related papers  (2024-06-20T10:46:50Z)
• Probabilistic Deep Metric Learning for Hyperspectral Image Classification [91.5747859691553]
  This paper proposes a probabilistic deep metric learning framework for hyperspectral image classification. It aims to predict the category of each pixel for an image captured by hyperspectral sensors. Our framework can be readily applied to existing hyperspectral image classification methods.
  arXiv  Detail & Related papers  (2022-11-15T17:57:12Z)
• MLIC: Multi-Reference Entropy Model for Learned Image Compression [28.63380127598021]
  We propose the Multi-Reference Entropy Model (MEM) and the advanced version, MEM$+$, to capture different types of correlations present in latent representation. Based on MEM and MEM$+$, we propose image compression models MLIC and MLIC$+$. Our MLIC and MLIC$+$ models achieve state-of-the-art performance, reducing BD-rate by $8.05%$ and $11.39%$ on the Kodak dataset compared to VTM-17.0 when measured in PSNR.
  arXiv  Detail & Related papers  (2022-11-14T11:07:18Z)
• Semantic Image Synthesis via Diffusion Models [174.24523061460704]
  Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in various image generation tasks.<n>Recent work on semantic image synthesis mainly follows the de facto GAN-based approaches.<n>We propose a novel framework based on DDPM for semantic image synthesis.
  arXiv  Detail & Related papers  (2022-06-30T18:31:51Z)
• Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules [22.818632387206257]
  Two key components of learned image compression are the entropy model of the latent representations and the encoding/decoding network architectures. We propose a more flexible discretized Gaussian-Laplacian-Logistic mixture model (GLLMM) for the latent representations. In the encoding/decoding network design part, we propose a residual blocks (CRB) where multiple residual blocks are serially connected with additional shortcut connections.
  arXiv  Detail & Related papers  (2021-07-14T02:54:22Z)
• A Feature Fusion-Net Using Deep Spatial Context Encoder and Nonstationary Joint Statistical Model for High Resolution SAR Image Classification [10.152675581771113]
  A novel end-to-end supervised classification method is proposed for HR SAR images. To extract more effective spatial features, a new deep spatial context encoder network (DSCEN) is proposed. To enhance the diversity of statistics, the nonstationary joint statistical model (NS-JSM) is adopted to form the global statistical features.
  arXiv  Detail & Related papers  (2021-05-11T06:20:14Z)
• PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization [64.39761523935613]
  We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding. It also exploits correlations between the different semantic levels of CNN to better localize anomalies.
  arXiv  Detail & Related papers  (2020-11-17T17:29:18Z)
• Prototype Mixture Models for Few-shot Semantic Segmentation [50.866870384596446]
  Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose. We propose prototype mixture models (PMMs), which correlate diverse image regions with multiple prototypes to enforce the prototype-based semantic representation. PMMs improve 5-shot segmentation performance on MS-COCO by up to 5.82% with only a moderate cost for model size and inference speed.
  arXiv  Detail & Related papers  (2020-08-10T04:33:17Z)
• Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty [32.33791302617957]
  We introduce segmentation networks (SSNs), an efficient probabilistic method for modelling aleatoric uncertainty with any image segmentation network architecture. SSNs can generate multiple spatially coherent hypotheses for a single image. We tested our method on the segmentation of real-world medical data, including lung nodules in 2D CT and brain tumours in 3D multimodal MRI scans.
  arXiv  Detail & Related papers  (2020-06-10T18:06:41Z)
• Probabilistic Semantic Segmentation Refinement by Monte Carlo Region Growing [0.7424262881242935]
  We introduce a fully unsupervised post-processing algorithm that exploits Monte Carlo sampling and pixel similarities to propagate high-confidence pixel labels into regions of low-confidence classification. Experiments using multiple modern semantic segmentation networks and benchmark datasets demonstrate the effectiveness of our approach for the refinement of segmentation predictions at different levels of coarseness.
  arXiv  Detail & Related papers  (2020-05-12T15:23:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.