Quantum medical image encoding and compression using Fourier-based methods

• URL: http://arxiv.org/abs/2505.06471v1
• Date: Fri, 09 May 2025 23:56:06 GMT
• Title: Quantum medical image encoding and compression using Fourier-based methods
• Authors: Taehee Ko, Inho Lee, Hyeong Won Yu,
• Abstract summary: We propose a quantum image encoding method that effectively reduces gates than the number of pixels by a factor of at least 4.<n>We demonstrate our method for various 1024 by 1024 high-quality medical images captured during the Bilateral Axillo-Breast Approach (BABA) robotic thyroidectomy surgery.
• Score: 3.035277272674123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum image processing (QIMP) has recently emerged as a promising field for modern image processing applications. In QIMP algorithms, encoding classical image informaiton into quantum circuit is important as the first step. However, most of existing encoding methods use gates almost twice the number of pixels in an image, and simulating even a modest sized image is computationally demanding. In this work, we propose a quantum image encoding method that effectively reduces gates than the number of pixels by a factor at least 4. We demonstrate our method for various 1024 by 1024 high-quality medical images captured during the Bilateral Axillo-Breast Approach (BABA) robotic thyroidectomy surgery. Additionally, two compression techniques are proposed to further reduce the number of gates as well as pre-processing time with negligible loss of image quality. We suggest our image encoding strategy as a valuable option for large scale medical imaging.

Related papers

• A Fast Quantum Image Compression Algorithm based on Taylor Expansion [0.0]
  In this study, we upgrade a quantum image compression algorithm within parameterized quantum circuits.<n>Our approach encodes image data as unitary operator parameters and applies the quantum compilation algorithm to emulate the encryption process.<n> Experimental results on benchmark images, including Lenna and Cameraman, show that our method achieves up to 86% reduction in the number of iterations.
  arXiv  Detail & Related papers  (2025-02-15T06:03:49Z)
• PALQA: A Novel Parameterized Position-Aware Lossy Quantum Autoencoder using LSB Control Qubit for Efficient Image Compression [7.340017786387768]
  This work introduces a novel parameterized position-aware lossy quantum autoencoder (PALQA) circuit that utilizes the least significant bit control qubit for image compression.<n>The PALQA circuit demonstrates superior performance in terms of the number of gates required and PSNR metrics.
  arXiv  Detail & Related papers  (2025-02-04T10:08:02Z)
• Quantization-aware Matrix Factorization for Low Bit Rate Image Compression [8.009813033356478]
  Lossy image compression is essential for efficient transmission and storage.<n>We introduce a quantization-aware matrix factorization (QMF) to develop a novel lossy image compression method.<n>Our method consistently outperforms JPEG at low bit rates below 0.25 bits per pixel (bpp) and remains comparable at higher bit rates.
  arXiv  Detail & Related papers  (2024-08-22T19:08:08Z)
• 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)
• Hybrid quantum transfer learning for crack image classification on NISQ hardware [62.997667081978825]
  We present an application of quantum transfer learning for detecting cracks in gray value images. We compare the performance and training time of PennyLane's standard qubits with IBM's qasm_simulator and real backends.
  arXiv  Detail & Related papers  (2023-07-31T14:45:29Z)
• A hybrid-qudit representation of digital RGB images [7.766921168069532]
  We use two entangled quantum registers constituting of total 7 qutrits to encode the color channels and their intensities. We generalize the existing encoding methods by using both qubits and qutrits to encode the pixel positions of a rectangular image. This hybrid-qudit approach aligns well with the current progress of NISQ devices in incorporating higher dimensional quantum systems.
  arXiv  Detail & Related papers  (2022-07-25T21:57:46Z)
• A hybrid quantum image edge detector for the NISQ era [62.997667081978825]
  We propose a hybrid method for quantum edge detection based on the idea of a quantum artificial neuron. Our method can be practically implemented on quantum computers, especially on those of the current noisy intermediate-scale quantum era.
  arXiv  Detail & Related papers  (2022-03-22T22:02:09Z)
• Improved FRQI on superconducting processors and its restrictions in the NISQ era [62.997667081978825]
  We study the feasibility of the Flexible Representation of Quantum Images (FRQI) We also check experimentally what is the limit in the current noisy intermediate-scale quantum era. We propose a method for simplifying the circuits needed for the FRQI.
  arXiv  Detail & Related papers  (2021-10-29T10:42:43Z)
• Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images [54.40852143927333]
  Lossy image compression is pervasively conducted to save communication bandwidth, resulting in undesirable compression artifacts. We propose a resource-efficient blind quality enhancement (RBQE) approach for compressed images. Our approach can automatically decide to terminate or continue enhancement according to the assessed quality of enhanced images.
  arXiv  Detail & Related papers  (2020-06-30T07:38:47Z)
• Quantization Guided JPEG Artifact Correction [69.04777875711646]
  We develop a novel architecture for artifact correction using the JPEG files quantization matrix. This allows our single model to achieve state-of-the-art performance over models trained for specific quality settings.
  arXiv  Detail & Related papers  (2020-04-17T00:10:08Z)
• Quantum Medical Imaging Algorithms [9.775834440292487]
  A central task in medical imaging is the reconstruction of an image or function from data collected by medical devices. We provide quantum algorithms for image reconstruction with exponential speedup over classical counterparts when data is input as a quantum state.
  arXiv  Detail & Related papers  (2020-04-04T22:19:22Z)

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.