An Efficient Compression Method For Sign Information Of DCT Coefficients Via Sign Retrieval

Telemedicine is one of the most emerging technologies of applied medical sciences. Medical information related to patients is transmitted and stored for references and consultations. Medical images occupy huge space; in order to transmit these images may delay the process of image transmission in critical times. Image compression techniques provide a better solution to combat bandwidth scarcity problems, and transmit same image in a much lower bandwidth requirements, more faster and at the same time maintain quality. In this paper a differential image compression method is developed in which medical images are taken from a wounded patient and are compressed to reduce the bit rate of these images. Results indicate that on average 25% compression on images is achieved with 3.5 MOS taken from medical doctors and other paramedical staff the ultimately user of the images.

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A robust and high capacity data hiding method for JPEG compressed images with SVD-based block selection and advanced error correcting techniques

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189877 ◽

2021 ◽

pp. 1-11

Author(s):

Kusan Biswas

Keyword(s):

Data Hiding ◽

High Capacity ◽

Error Correcting Codes ◽

Human Vision ◽

Data Embedding ◽

Compressed Images ◽

Matrix Encoding ◽

Dct Coefficients ◽

Capacity Data ◽

Value Decomposition

In this paper, we propose a frequency domain data hiding method for the JPEG compressed images. The proposed method embeds data in the DCT coefficients of the selected 8 × 8 blocks. According to the theories of Human Visual Systems (HVS), human vision is less sensitive to perturbation of pixel values in the uneven areas of the image. In this paper we propose a Singular Value Decomposition based image roughness measure (SVD-IRM) using which we select the coarse 8 × 8 blocks as data embedding destinations. Moreover, to make the embedded data more robust against re-compression attack and error due to transmission over noisy channels, we employ Turbo error correcting codes. The actual data embedding is done using a proposed variant of matrix encoding that is capable of embedding three bits by modifying only one bit in block of seven carrier features. We have carried out experiments to validate the performance and it is found that the proposed method achieves better payload capacity and visual quality and is more robust than some of the recent state-of-the-art methods proposed in the literature.

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3D DCT Based Image Compression Method for the Medical Endoscopic Application

Sensors ◽

10.3390/s21051817 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1817

Author(s):

Jiawen Xue ◽

Li Yin ◽

Zehua Lan ◽

Mingzhu Long ◽

Guolin Li ◽

...

Keyword(s):

Image Compression ◽

Compression Ratio ◽

Signal To Noise Ratio ◽

Blocking Artifacts ◽

Compression Method ◽

High Compression Ratio ◽

Multiplication Operation ◽

3D Data ◽

Wireless Capsule

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

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