Implicit Neural Image Field for Biological Microscopy Image Compression

• URL: http://arxiv.org/abs/2405.19012v1
• Date: Wed, 29 May 2024 11:51:33 GMT
• Title: Implicit Neural Image Field for Biological Microscopy Image Compression
• Authors: Gaole Dai, Cheng-Ching Tseng, Qingpo Wuwu, Rongyu Zhang, Shaokang Wang, Ming Lu, Tiejun Huang, Yu Zhou, Ali Ata Tuz, Matthias Gunzer, Jianxu Chen, Shanghang Zhang,
• Abstract summary: We propose an adaptive compression workflow based on Implicit Neural Representation (INR) This approach permits application-specific compression objectives, capable of compressing images of any shape and arbitrary pixel-wise decompression. We demonstrated on a wide range of microscopy images that our workflow not only achieved high, controllable compression ratios but also preserved detailed information critical for downstream analysis.
• Score: 37.0218688308699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid pace of innovation in biological microscopy imaging has led to large images, putting pressure on data storage and impeding efficient sharing, management, and visualization. This necessitates the development of efficient compression solutions. Traditional CODEC methods struggle to adapt to the diverse bioimaging data and often suffer from sub-optimal compression. In this study, we propose an adaptive compression workflow based on Implicit Neural Representation (INR). This approach permits application-specific compression objectives, capable of compressing images of any shape and arbitrary pixel-wise decompression. We demonstrated on a wide range of microscopy images from real applications that our workflow not only achieved high, controllable compression ratios (e.g., 512x) but also preserved detailed information critical for downstream analysis.

Related papers

• Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution [33.69980388844034]
  In this paper, we show that the commonly used JPEG algorithm is not best suited for further compression. We propose Stain Quantized Latent Compression, a novel DL based histopathology data compression approach. We show that our approach yields superior performance in a classification downstream task, compared to traditional approaches like JPEG.
  arXiv  Detail & Related papers  (2024-06-18T13:47:17Z)
• UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation [59.3877309501938]
  Implicit Neural Representation (INR) networks have shown remarkable versatility due to their flexible compression ratios. We introduce a codebook containing frequency domain information as a prior input to the INR network. This enhances the representational power of INR and provides distinctive conditioning for different image blocks.
  arXiv  Detail & Related papers  (2024-05-27T05:52:13Z)
• MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model [78.4051835615796]
  This paper proposes a method called Multimodal Image Semantic Compression. It consists of an LMM encoder for extracting the semantic information of the image, a map encoder to locate the region corresponding to the semantic, an image encoder generates an extremely compressed bitstream, and a decoder reconstructs the image based on the above information. It can achieve optimal consistency and perception results while saving perceptual 50%, which has strong potential applications in the next generation of storage and communication.
  arXiv  Detail & Related papers  (2024-02-26T17:11:11Z)
• Transferable Learned Image Compression-Resistant Adversarial Perturbations [66.46470251521947]
  Adversarial attacks can readily disrupt the image classification system, revealing the vulnerability of DNN-based recognition tasks. We introduce a new pipeline that targets image classification models that utilize learned image compressors as pre-processing modules.
  arXiv  Detail & Related papers  (2024-01-06T03:03:28Z)
• Streaming Lossless Volumetric Compression of Medical Images Using Gated Recurrent Convolutional Neural Network [0.0]
  This paper introduces a hardware-friendly streaming lossless volumetric compression framework. We propose a gated recurrent convolutional neural network that combines diverse convolutional structures and fusion gate mechanisms. Our method exhibits robust generalization ability and competitive compression speed.
  arXiv  Detail & Related papers  (2023-11-27T07:19:09Z)
• Image Compression and Decompression Framework Based on Latent Diffusion Model for Breast Mammography [0.0]
  This research presents a novel framework for the compression and decompression of medical images utilizing the Latent Diffusion Model (LDM) The LDM represents advancement over the denoising diffusion probabilistic model (DDPM) with a potential to yield superior image quality. A possible application of LDM and Torchvision for image upscaling has been explored using medical image data.
  arXiv  Detail & Related papers  (2023-10-08T22:08:59Z)
• Machine Perception-Driven Image Compression: A Layered Generative Approach [32.23554195427311]
  layered generative image compression model is proposed to achieve high human vision-oriented image reconstructed quality. Task-agnostic learning-based compression model is proposed, which effectively supports various compressed domain-based analytical tasks. Joint optimization schedule is adopted to acquire best balance point among compression ratio, reconstructed image quality, and downstream perception performance.
  arXiv  Detail & Related papers  (2023-04-14T02:12:38Z)
• Estimating the Resize Parameter in End-to-end Learned Image Compression [50.20567320015102]
  We describe a search-free resizing framework that can further improve the rate-distortion tradeoff of recent learned image compression models. Our results show that our new resizing parameter estimation framework can provide Bjontegaard-Delta rate (BD-rate) improvement of about 10% against leading perceptual quality engines.
  arXiv  Detail & Related papers  (2022-04-26T01:35:02Z)
• 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)
• Discernible Image Compression [124.08063151879173]
  This paper aims to produce compressed images by pursuing both appearance and perceptual consistency. Based on the encoder-decoder framework, we propose using a pre-trained CNN to extract features of the original and compressed images. Experiments on benchmarks demonstrate that images compressed by using the proposed method can also be well recognized by subsequent visual recognition and detection models.
  arXiv  Detail & Related papers  (2020-02-17T07:35:08Z)
• 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.