ROI-based Deep Image Compression with Implicit Bit Allocation

• URL: http://arxiv.org/abs/2511.08918v1
• Date: Thu, 13 Nov 2025 01:17:44 GMT
• Title: ROI-based Deep Image Compression with Implicit Bit Allocation
• Authors: Kai Hu, Han Wang, Renhe Liu, Zhilin Li, Shenghui Song, Yu Liu,
• Abstract summary: Region of Interest (ROI)-based image compression has rapidly developed due to its ability to maintain high fidelity in important regions.<n>Existing compression methods apply masks to suppress background information before quantization.<n>This work proposes an efficient ROI-based deep image compression model with implicit bit allocation.
• Score: 15.62284701009422
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Region of Interest (ROI)-based image compression has rapidly developed due to its ability to maintain high fidelity in important regions while reducing data redundancy. However, existing compression methods primarily apply masks to suppress background information before quantization. This explicit bit allocation strategy, which uses hard gating, significantly impacts the statistical distribution of the entropy model, thereby limiting the coding performance of the compression model. In response, this work proposes an efficient ROI-based deep image compression model with implicit bit allocation. To better utilize ROI masks for implicit bit allocation, this paper proposes a novel Mask-Guided Feature Enhancement (MGFE) module, comprising a Region-Adaptive Attention (RAA) block and a Frequency-Spatial Collaborative Attention (FSCA) block. This module allows for flexible bit allocation across different regions while enhancing global and local features through frequencyspatial domain collaboration. Additionally, we use dual decoders to separately reconstruct foreground and background images, enabling the coding network to optimally balance foreground enhancement and background quality preservation in a datadriven manner. To the best of our knowledge, this is the first work to utilize implicit bit allocation for high-quality regionadaptive coding. Experiments on the COCO2017 dataset show that our implicit-based image compression method significantly outperforms explicit bit allocation approaches in rate-distortion performance, achieving optimal results while maintaining satisfactory visual quality in the reconstructed background regions.

Related papers

• SANR: Scene-Aware Neural Representation for Light Field Image Compression with Rate-Distortion Optimization [54.184486302645716]
  We propose a Scene-Aware Neural Representation framework for light field image compression with end-to-end rate-distortion optimization.<n>For scene awareness, SANR introduces a hierarchical scene modeling block that leverages multi-scale latent codes to capture intrinsic scene structures.<n>Experiment results demonstrate that SANR significantly outperforms state-of-the-art techniques regarding rate-distortion performance with a 65.62% BD-rate saving against HEVC.
  arXiv  Detail & Related papers  (2025-10-17T16:00:43Z)
• Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion [28.61304513668606]
  ResULIC is a residual-guided ultra lowrate image compression system.<n>It incorporates residual signals into both semantic retrieval and the diffusion-based generation process.<n>It achieves superior objective and subjective performance compared to state-of-the-art diffusion-based methods.
  arXiv  Detail & Related papers  (2025-05-13T06:51:23Z)
• Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaptation [52.82508784748278]
  This paper proposes a Control Generative Image Compression framework, termed Control-GIC.<n>Control-GIC is capable of fine-grained adaption across a broad spectrum while ensuring high-fidelity and generality compression.<n>Our experiments show that Control-GIC allows highly flexible and controllable adaption where the results demonstrate its superior performance over recent state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-06-02T14:22:09Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• You Can Mask More For Extremely Low-Bitrate Image Compression [80.7692466922499]
  Learned image compression (LIC) methods have experienced significant progress during recent years. LIC methods fail to explicitly explore the image structure and texture components crucial for image compression. We present DA-Mask that samples visible patches based on the structure and texture of original images. We propose a simple yet effective masked compression model (MCM), the first framework that unifies LIC and LIC end-to-end for extremely low-bitrate compression.
  arXiv  Detail & Related papers  (2023-06-27T15:36:22Z)
• Beyond Learned Metadata-based Raw Image Reconstruction [86.1667769209103]
  Raw images have distinct advantages over sRGB images, e.g., linearity and fine-grained quantization levels. They are not widely adopted by general users due to their substantial storage requirements. We propose a novel framework that learns a compact representation in the latent space, serving as metadata.
  arXiv  Detail & Related papers  (2023-06-21T06:59:07Z)
• Exploring Resolution Fields for Scalable Image Compression with Uncertainty Guidance [47.96024424475888]
  In this work, we explore the potential of resolution fields in scalable image compression. We propose the reciprocal pyramid network (RPN) that fulfills the need for more adaptable and versatile compression. Experiments show the superiority of RPN against existing classical and deep learning-based scalable codecs.
  arXiv  Detail & Related papers  (2023-06-15T08:26:24Z)
• Implicit Neural Representations for Image Compression [103.78615661013623]
  Implicit Neural Representations (INRs) have gained attention as a novel and effective representation for various data types. We propose the first comprehensive compression pipeline based on INRs including quantization, quantization-aware retraining and entropy coding. We find that our approach to source compression with INRs vastly outperforms similar prior work.
  arXiv  Detail & Related papers  (2021-12-08T13:02:53Z)
• Generalized Octave Convolutions for Learned Multi-Frequency Image Compression [20.504561050200365]
  We propose the first learned multi-frequency image compression and entropy coding approach. It is based on the recently developed octave convolutions to factorize the latents into high and low frequency (resolution) components. We show that the proposed generalized octave convolution can improve the performance of other auto-encoder-based computer vision tasks.
  arXiv  Detail & Related papers  (2020-02-24T01:35:29Z)
• A GAN-based Tunable Image Compression System [13.76136694287327]
  This paper rethinks content-based compression by using Generative Adversarial Network (GAN) to reconstruct the non-important regions. A tunable compression scheme is also proposed in this paper to compress an image to any specific compression ratio without retraining the model.
  arXiv  Detail & Related papers  (2020-01-18T02:40:09Z)

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.