Recent years has witnessed growing interest in hyperspectral image (HSI) processing. In practice, however, HSIs always suffer from huge data size and mass of redundant information, which hinder their application in many cases. HSI compression is a straightforward way of relieving these problems. However, most of the conventional image encoding algorithms mainly focus on the spatial dimensions, and they need not consider the redundancy in the spectral dimension. In this paper, we propose a novel HSI compression and reconstruction algorithm via patch-based low-rank tensor decomposition (PLTD). Instead of processing the HSI separately by spectral channel or by pixel, we represent each local patch of the HSI as a third-order tensor. Then, the similar tensor patches are grouped by clustering to form a fourth-order tensor per cluster. Since the grouped tensor is assumed to be redundant, each cluster can be approximately decomposed to a coefficient tensor and three dictionary matrices, which leads to a low-rank tensor representation of both the spatial and spectral modes. The reconstructed HSI can then be simply obtained by the product of the coefficient tensor and dictionary matrices per cluster. In this way, the proposed PLTD algorithm simultaneously removes the redundancy in both the spatial and spectral domains in a unified framework. The extensive experimental results on various public HSI datasets demonstrate that the proposed method outperforms the traditional image compression approaches and other tensor-based methods.

PLTD: Patch-Based Low-Rank Tensor Decomposition for Hyperspectral Images

In teleradiology, image contents may be altered due to noisy communication channels and hacker manipulation. Medical image data is very sensitive and can not tolerate any illegal change. Illegally changed image-based analysis could result in wrong medical decision. Digital watermarking technique can be used to authenticate images and detect as well as recover illegal changes made to teleradiology images. Watermarking of medical images with heavy payload watermarks causes image perceptual degradation. The image perceptual degradation directly affects medical diagnosis. To maintain the image perceptual and diagnostic qualities standard during watermarking, the watermark should be lossless compressed. This paper focuses on watermarking of ultrasound medical images with Lempel-Ziv-Welch (LZW) lossless-compressed watermarks. The watermark lossless compression reduces watermark payload without data loss. In this research work, watermark is the combination of defined region of interest (ROI) and image watermarking secret key. The performance of the LZW compression technique was compared with other conventional compression methods based on compression ratio. LZW was found better and used for watermark lossless compression in ultrasound medical images watermarking. Tabulated results show the watermark bits reduction, image watermarking with effective tamper detection and lossless recovery.

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Watermark Compression in Medical Image Watermarking Using Lempel-Ziv-Welch (LZW) Lossless Compression Technique.

Data processing with multiple domains is an important concept in any platform; it deals with multimedia and textual information. Where textual data processing focuses on a structured or unstructure...

Review of video compression techniques based on fractal transform function and swarm intelligence

Image compression is an implementation of the data compression which encodes actual image with some bits. Thepurpose of the image compression is to decrease the redundancy and irrelevance of image data to be capable to record or send data in an effective form. Hence the image compression decreases the time of transmit in the network and raises the transmission speed. In Lossless technique of image compression, no data get lost while doing the compression. To solve these types of issues various techniques for the image compression are used. Now questions like how to do mage compression and second one is which types of technology is used, may be arises. For this reason commonly two types’ of approaches are explained called as lossless and the lossy image compression approaches. These techniques are easy in their applications and consume very little memory. An algorithm has also been introduced and applied to compress images and to decompress them back, by using the Huffman encoding techniques.

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Various Image Compression Techniques: Lossy and Lossless

The security of images transferred via internet is very important issue. The proposed system tries to satisfy all requirements of security to ensure that any medical image related to any patient do not allow to be accessed via any unauthorized person. We can use an encryption scheme for encrypt medical image. Only the authorized person can decrypt this medical image and can obtain the original image. The ownership of these medical images is very important to improve. We can identify the ownership of any medical image by using watermark related to the owner of this medical image. We used name of the patient and serial number. Capture the ear image of the owner then extract features from ear after that encrypt those features then used it as watermark. The size of the medical image is very effective point in transmitting via internet. Because of this the proposed system used mix of compression techniques applied on medical image before sending via internet. Run time is very important in any system and complexity is very important aspect in computer science. To reduce run time and complexity of the proposed system, we can use DWT to separates an image into approximation image LL, HL LH and HH.

Improving The Security of Images Transmission

The development of multimedia and digital imaging has led to high quantity of data required to represent modern imagery. This requires large disk space for storage, and long time for transmission over computer networks, and these two are relatively expensive. These factors prove the need for images compression. Image compression addresses the problem of reducing the amount of space required to represent a digital image yielding a compact representation of an image, and thereby reducing the image storage/transmission time requirements. The key idea here is to remove redundancy of data presented within an image to reduce its size without affecting the essential information of it. We are concerned with lossless image compression in this paper. Our proposed approach is a mix of a number of already existing techniques. Our approach works as follows: first, we apply the well-known Lempel-Ziv-Welch (LZW) algorithm on the image in hand. What comes out of the first step is forward to the second step where the Bose, Chaudhuri and Hocquenghem (BCH) error correction and detected algorithm is used. To improve the compression ratio, the proposed approach applies the BCH algorithms repeatedly until “inflation” is detected. The experimental results show that the proposed algorithm could achieve an excellent compression ratio without losing data when compared to the standard compression algorithms.

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S. Al-Hashemi

Papers

Lossless Image Compression Technique Using Combination Methods