New Approaches for Image Compression Using Neural Network

Gaidhane, Vilas H.; Singh, Vijander; Hote, Yogesh V.; Kumar, Mahendra

doi:10.4236/jilsa.2011.34025

Cited by 16 publications

(4 citation statements)

References 16 publications

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“…For medical image compression, only lossless schemes are applicable because this is very sensitive data and any data loss is not affordable, as it can lead to wrong medical decision making. 22,23 We have developed TDARWMI, tamper detection, localization, and recovery of medical image watermarking scheme with watermark lossless compression using LZW technique 24,25 to overcome the drawbacks of TALLOR-RSMA scheme. For this purpose, image ROI is defined, and its SHA-256 hash value is calculated.…”

Section: Methodsmentioning

confidence: 99%

Watermarking of ultrasound medical images in teleradiology using compressed watermark

et al. 2016

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The open accessibility of Internet-based medical images in teleradialogy face security threats due to the nonsecured communication media. This paper discusses the spatial domain watermarking of ultrasound medical images for content authentication, tamper detection, and lossless recovery. For this purpose, the image is divided into two main parts, the region of interest (ROI) and region of noninterest (RONI). The defined ROI and its hash value are combined as watermark, lossless compressed, and embedded into the RONI part of images at pixel's least significant bits (LSBs). The watermark lossless compression and embedding at pixel's LSBs preserve image diagnostic and perceptual qualities. Different lossless compression techniques including Lempel-Ziv-Welch (LZW) were tested for watermark compression. The performances of these techniques were compared based on more bit reduction and compression ratio. LZW was found better than others and used in tamper detection and recovery watermarking of medical images (TDARWMI) scheme development to be used for ROI authentication, tamper detection, localization, and lossless recovery. TDARWMI performance was compared and found to be better than other watermarking schemes.

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Section: Methodsmentioning

confidence: 99%

Watermarking of ultrasound medical images in teleradiology using compressed watermark

et al. 2016

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“…The transmitter encodes and then transmits the output of the hidden layer 16 values as com-pared to the 64 values of the original image. The receiver receives and decodes the 16 hidden neurons output and generates the 64 outputs at the output layer [22].…”

Section: Fig 3 Fractal Image Compression Stepsmentioning

confidence: 99%

Image Compression Technique Based on Fractal Image Compression Using Neural Network – A Review

Naaman

2021

AJRCoS

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Image compression research has increased dramatically as a result of the growing demands for image transmission in computer and mobile environments. It is needed especially for reduced storage and efficient image transmission and used to reduce the bits necessary to represent a picture digitally while preserving its original quality. Fractal encoding is an advanced technique of image compression. It is based on the image's forms as well as the generation of repetitive blocks via mathematical conversions. Because of resources needed to compress large data volumes, enormous programming time is needed, therefore Fractal Image Compression's main disadvantage is a very high encoding time where decoding times are extremely fast. An artificial intelligence technique similar to a neural network is used to reduce the search space and encoding time for images by employing a neural network algorithm known as the “back propagation” neural network algorithm. Initially, the image is divided into fixed-size and domains. For each range block its most matched domain is selected, its range index is produced and best matched domains index is the expert system's input, which reduces matching domain blocks in sets of results. This leads in the training of the neural network. This trained network is now used to compress other images which give encoding a lot less time. During the decoding phase, any random original image, converging after some changes to the Fractal image, reciprocates the transformation parameters. The quality of this FIC is indeed demonstrated by the simulation findings. This paper explores a unique neural network FIC that is capable of increasing neural network speed and image quality simultaneously.

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“…However, the selection of the optimization techniques is totally depends on the nature of error function. The Levenberg-Marquardt algorithm is a modification of Newton's method specially designed for minimizing the error function that is sum of squares of non-linear functions [29][30][31][32]. This is very well suited to neural network training, where the mean squared error is used as the performance index.…”

Section: Emotion Classificationmentioning

confidence: 99%

Emotion recognition using eigenvalues and Levenberg–Marquardt algorithm-based classifier

2016

Self Cite

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In this paper, a simple and computationally efficient approach is proposed for person independent facial emotion recognition. The proposed approach is based on the significant features of an image, i.e., the collection of few largest eigenvalues (LE). Further, a Levenberg-Marquardt algorithm-based neural network (LMNN) is applied for multiclass emotions classification. This leads to a new facial emotion recognition approach (LE-LMNN) which is systematically examined on JAFFE and Cohn-Kanade databases. Experimental results illustrate that the LE-LMNN approach is effective and computationally efficient for facial emotion recognition. The robustness of the proposed approach is also tested on low-resolution facial emotion images. The performance of the proposed approach is found to be superior as compared to the various existing methods.

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New Approaches for Image Compression Using Neural Network

Abstract: ABSTRACT

Cited by 16 publications

References 16 publications

Watermarking of ultrasound medical images in teleradiology using compressed watermark

Watermarking of ultrasound medical images in teleradiology using compressed watermark

Image Compression Technique Based on Fractal Image Compression Using Neural Network – A Review

Emotion recognition using eigenvalues and Levenberg–Marquardt algorithm-based classifier

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