Deep data compression for approximate ultrasonic image formation

• URL: http://arxiv.org/abs/2009.02293v1
• Date: Fri, 4 Sep 2020 16:43:12 GMT
• Title: Deep data compression for approximate ultrasonic image formation
• Authors: Georgios Pilikos, Lars Horchens, Kees Joost Batenburg, Tristan van Leeuwen, Felix Lucka
• Abstract summary: In ultrasonic imaging systems, data acquisition and image formation are performed on separate computing devices. Deep neural networks are optimized to preserve the image quality of a particular image formation method.
• Score: 1.0266286487433585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In many ultrasonic imaging systems, data acquisition and image formation are performed on separate computing devices. Data transmission is becoming a bottleneck, thus, efficient data compression is essential. Compression rates can be improved by considering the fact that many image formation methods rely on approximations of wave-matter interactions, and only use the corresponding part of the data. Tailored data compression could exploit this, but extracting the useful part of the data efficiently is not always trivial. In this work, we tackle this problem using deep neural networks, optimized to preserve the image quality of a particular image formation method. The Delay-And-Sum (DAS) algorithm is examined which is used in reflectivity-based ultrasonic imaging. We propose a novel encoder-decoder architecture with vector quantization and formulate image formation as a network layer for end-to-end training. Experiments demonstrate that our proposed data compression tailored for a specific image formation method obtains significantly better results as opposed to compression agnostic to subsequent imaging. We maintain high image quality at much higher compression rates than the theoretical lossless compression rate derived from the rank of the linear imaging operator. This demonstrates the great potential of deep ultrasonic data compression tailored for a specific image formation method.

Related papers

• Perceptual Image Compression with Cooperative Cross-Modal Side Information [53.356714177243745]
  We propose a novel deep image compression method with text-guided side information to achieve a better rate-perception-distortion tradeoff. Specifically, we employ the CLIP text encoder and an effective Semantic-Spatial Aware block to fuse the text and image features.
  arXiv  Detail & Related papers  (2023-11-23T08:31:11Z)
• Neural-based Compression Scheme for Solar Image Data [8.374518151411612]
  We propose a neural network-based lossy compression method to be used in NASA's data-intensive imagery missions. In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image. As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole (CH) detection using our compressed images.
  arXiv  Detail & Related papers  (2023-11-06T04:13:58Z)
• Deep learning based Image Compression for Microscopy Images: An Empirical Study [3.915183869199319]
  This study analyzes classic and deep learning based image compression methods, and their impact on deep learning based image processing models. To compress images in such a wanted way, multiple classical lossy image compression techniques are compared to several AI-based compression models. We found that AI-based compression techniques largely outperform the classic ones and will minimally affect the downstream label-free task in 2D cases.
  arXiv  Detail & Related papers  (2023-11-02T16:00:32Z)
• CompaCT: Fractal-Based Heuristic Pixel Segmentation for Lossless Compression of High-Color DICOM Medical Images [0.0]
  Medical images require a high color depth of 12 bits per pixel component for accurate analysis by physicians. Standard-based compression of images via filtering is well-known; however, it remains suboptimal in the medical domain due to non-specialized implementations. This study proposes a medical image compression algorithm, CompaCT, that aims to target spatial features and patterns of pixel concentration for dynamically enhanced data processing.
  arXiv  Detail & Related papers  (2023-08-24T21:43:04Z)
• Convolutional Neural Network (CNN) to reduce construction loss in JPEG compression caused by Discrete Fourier Transform (DFT) [0.0]
  Convolutional Neural Networks (CNN) have received more attention than most other types of deep neural networks. In this work, an effective image compression method is purposed using autoencoders.
  arXiv  Detail & Related papers  (2022-08-26T12:46:16Z)
• Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training [90.76576712433595]
  Applying lossy compression on images processed by deep neural networks can lead to significant accuracy degradation. Inspired by the curriculum learning paradigm, we present a novel training approach called curriculum pre-training (CPT) for crowd counting on compressed images.
  arXiv  Detail & Related papers  (2022-08-15T08:43:21Z)
• Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications [78.55896581882595]
  Lossy image compression techniques can reduce the quality of the images, leading to accuracy degradation. In this paper, we analyze the effect of applying low-overhead lossy image compression methods on the accuracy of visual crowd counting.
  arXiv  Detail & Related papers  (2022-07-20T19:20:03Z)
• Modeling Image Quantization Tradeoffs for Optimal Compression [0.0]
  Lossy compression algorithms target tradeoffs by quantizating high frequency data to increase compression rates. We propose a new method of optimizing quantization tables using Deep Learning and a minimax loss function.
  arXiv  Detail & Related papers  (2021-12-14T07:35:22Z)
• 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)
• Modeling Lost Information in Lossy Image Compression [72.69327382643549]
  Lossy image compression is one of the most commonly used operators for digital images. We propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem.
  arXiv  Detail & Related papers  (2020-06-22T04:04:56Z)
• 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)

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.