New progressive image transmission based on quadtree and shading approach with resolution control

Chung, Kuo–Liang; Tseng, Shou-Yi

doi:10.1016/s0167-8655(01)00106-4

Cited by 14 publications

(4 citation statements)

References 13 publications

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“…Quadtree decomposition has a feature-preserving capability [ 11 ] and it can extract the details of images, thus more feature information can be extracted from the visual important ROIs than from the monotone areas of the image[ 39 ]. For instance, let X be the original image and we divide X up to p levels using quadtree decomposition.…”

Section: Proposed Roi-stitching Algorithmmentioning

confidence: 99%

Interpreting SVM for medical images using Quadtree

Shukla

Verma

Abhishek

et al. 2020

Multimed Tools Appl

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In this paper, we propose a quadtree based approach to capture the spatial information of medical images for explaining nonlinear SVM prediction. In medical image classification, interpretability becomes important to understand why the adopted model works. Explaining an SVM prediction is difficult due to implicit mapping done in kernel classification is uninformative about the position of data points in the feature space and the nature of the separating hyperplane in the original space. The proposed method finds ROIs which contain the discriminative regions behind the prediction. Localization of the discriminative region in small boxes can help in interpreting the prediction by SVM. Quadtree decomposition is applied recursively before applying SVMs on sub images and model identified ROIs are highlighted. Pictorial results of experiments on various medical image datasets prove the effectiveness of this approach. We validate the correctness of our method by applying occlusion methods.

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Section: Proposed Roi-stitching Algorithmmentioning

confidence: 99%

Interpreting SVM for medical images using Quadtree

Shukla

Verma

Abhishek

et al. 2020

Multimed Tools Appl

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“…As a result, the decoding post may not receive many shadows instantly, while the authorized person or group is eager to know what the image looks like. Again, progressive reconstruction through the available shadows is useful here ͑which is somehow different from the commonly seen progressive image transmission methods [7][8][9] because any of our generated shadows could be the missing shadow͒. We therefore wish to propose in this paper a progressive-viewing method to share the images.…”

Section: Motivation and Goalsmentioning

confidence: 99%

Progressive sharing of an image

2008

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We propose a sharing method to progressively reveal a given important image in the recovery phase. In the encoding phase, the distributor utilizes the three frequency bands ͑low, middle, and high͒ of the given image to generate shadows according to three prespecified thresholds. In the recovery phase, the secret image cannot be revealed if the number of shadows a team collects is less than the lowest threshold. However, when the number of collected shadows reaches the prespecified low ͑or middle-or high͒ threshold, the team can reconstruct a low-͑or middle-or high-͒ quality version of the secret image. In other words, the quality of the reconstructed image depends only on the number of shadows being received, rather than on which of the generated shadows are received. Each noise-like shadow is so small that it can be hidden in an ordinary image that is still several times smaller than the original image. ©

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“…e bit-plane progressive image transmission method (BPM) [5,6] is one of the low computational complexity and intuitive technique to transmit the image bit-plane by bit-plane which is detailed in Section 2. Another sort of progressive image transmission techniques is that, in [7][8][9][10][11][12], the image is divided into nonoverlapping blocks, and a quantized pixel value is used to represent the whole block, then, progressively dividing the image into finer blocks to improve the image resolution. Some schemes [13][14][15][16][17] transform the image into a wavelet domain and transmit the coefficients instead of pixels.…”

Section: Introductionmentioning

confidence: 99%

Progressive Transmission of Medical Images via a Bank of Generative Adversarial Networks

Chang

Wang

Horng

et al. 2021

Journal of Healthcare Engineering

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The healthcare sector is currently undergoing a major transformation due to the recent advances in deep learning and artificial intelligence. Despite a significant breakthrough in medical imaging and diagnosis, there are still many open issues and undeveloped applications in the healthcare domain. In particular, transmission of a large volume of medical images proves to be a challenging and time-consuming problem, and yet no prior studies have investigated the use of deep neural networks towards this task. The purpose of this paper is to introduce and develop a deep-learning approach for the efficient transmission of medical images, with a particular interest in the progressive coding of bit-planes. We establish a connection between bit-plane synthesis and image-to-image translation and propose a two-step pipeline for progressive image transmission. First, a bank of generative adversarial networks is trained for predicting bit-planes in a top-down manner, and then prediction residuals are encoded with a tailored adaptive lossless compression algorithm. Experimental results validate the effectiveness of the network bank for generating an accurate low-order bit-plane from high-order bit-planes and demonstrate an advantage of the tailored compression algorithm over conventional arithmetic coding for this special type of prediction residuals in terms of compression ratio.

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New progressive image transmission based on quadtree and shading approach with resolution control

Cited by 14 publications

References 13 publications

Interpreting SVM for medical images using Quadtree

Interpreting SVM for medical images using Quadtree

Progressive sharing of an image

Progressive Transmission of Medical Images via a Bank of Generative Adversarial Networks

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