The Application of Selective Image Compression Techniques

Digital medical images like X-Ray, Magnetic Resonance Imaging (MRI), Ultrasound, Computed Tomography (CT) are extensively used in diagnosis. The ease of storing and transmission of digital medical images is a boon to patients and medical professionals. Due to the large volume of images, image compression is required to accomplish fast and efficient transmission and reduction in storage space of medical images. Compression techniques used are very important while compressing digital medical images as the region of interest for diagnosis is generally small when compared to the whole image captured. Lossless compression techniques compress with no data loss but have low compression rate and lossy compression techniques can compress at high compression ratio but with a slight loss of data. Using lossless techniques in medical image does not give enough advantage in transmission and storage and lossy techniques may lose crucial data required for diagnosis. To maximize compression, in this paper it is proposed to investigate multiple compression techniques based on Region of Interest (ROI). In this paper a novel active contour method is proposed which is adaptive and marks the ROI without edges. The marked area of ROI is compressed using lossless compression and the other areas of the image are compressed using lossy wavelet compression techniques. The proposed procedure when applied on diverse MRI images, achieved an overall compression ratio of 69-81% without loss in the originality of ROI.

Over the period of time, the measure of data that is dealt by machines has increased rapidly. Consequently the storage space or memory required to store the digital image component of multimedia systems has become a significant issue. Therefore; image compression addresses this issue by reducing the measure of data required to present a digital image. Image can be compressed by Lossless or Lossy compression technique. In this paper, we are analyzing the lossy image compression technique. And, the type of lossy compression technique analyzed is ‘Transform Coding’. In transform coding, we analyze Hyper Analytic wavelet transform (HWT) and Huffman coding algorithm on FPGA. The architecture for HWT is designed using Verilog-HDL language and synthesized on Xilinx ISE tool such that performance, power consumption and area utilized are analyzed. The power consumed by the designed system is evaluated by the Xpower analysis tool, which yields 345mW at 100MHz clock frequency

An improved medical image compression technique with lossless region of interest

In many video applications fractal video compression is use for video coding caused by its different features and lower bit rate. Self similarity concepts of image compression are used in fractal video compression. However selfsimilarity means that fractal picture is consists of duplicates of itself that are interpreted and indicated by a change. More computational complexity is present in fractal video compression for reducing this complexity different technique has been implemented. In video compression, finding the motion vectors (MV) is one of the major factor in motion estimation, due to its high computation complexity allows in between the frames. Many application like multimedia service contains the temporal type of redundancies for emission of video i.e. storage space, bandwidth and transmission cost to reduces this kind of redundancy the motion estimation is used while not degrade a quality of the video. There are number of algorithm has been evolved for fast block based matching techniques in motion estimation to remonstrate the drawbacks relate to diminishing the number of searching point, complexities and computational cost etc., by reason of its effortlessness the block-based technique is demand in motion estimation. Block matching algorithms attracts many researchers from algorithms.the different domain for motion vector estimation also for solving real life applications in motion estimation for video coding. This paper laborite a review of various fractal compression techniques and block matching motion estimation purpose. So, transmission of video takes more time to reach its destination. Therefore, some video compression techniques are involved to remove the redundancy that present in original video. In continuation of fractal image compression uses fractal video compression technique. One of the image compression methods is fractal coding [1]. Its clam is that within a given local region the correlation not only presents in adjacent pixels, but also in global regions or different regions. Mainly video compression technique contains two types of technique i.e. lossy and lossless compression [2]. In lossless technique, reconstruction of total original data is possible. Due to this characteristic, most lossless compression technique referred it for data and executable files etc. But few data may be removed permanently in lossy compression. Mainly two types of redundancies are evolving in sequence of video they are temporal redundancy & spatial redundancy. Spatial redundancies define as correlation present in a frame among neighboring pixel value. Temporal redundancy means by considering a redundancy present in between adjacent frames of images in video. The interframe coding concept uses to lower the temporal redundancy. Similarly, the intraframe coding concept lower the spatial type of redundancy.

This study presents a new approach for lossy medical image compression using vector quantization. Recently, the digital image has been a reliable replacement for a hard copy of medical images, therefore, an effort has been made to ensure maintaining high-quality images to use for archiving, classification, or automated diagnostics support. Although the medical application contains all sorts of the images like microscopic, X-rays, tomography, and fiber optics imaging by angioplasty, all of this comes at the cost of using digital storage that needs to be regularly backed up and maintained and to help minimize the need for larger storage media, this study is focusing on applying Non-Decimated Wavelet Transform (NDWT) and combined lossy and lossless compression techniques that will allow the images to take much smaller storage space while maintaining the high level of quality for these images. This study is focusing on chest X-ray images compression using a combination of lossy compression techniques using two Vector Quantization (VQ) algorithms such as k-means clustering and Linde, Buzo, and Gray (LBG) algorithm, and three lossless compression techniques such as Arithmetic Coding (AC), Run Length Encoding (RLE) and Huffman Coding (HC) and choose the optimum combination of them. Then, the performance is measured using Compression Ratio (CR), processing time, or called run time, Peak Signal to Noise Ratio (PSNR), and Bit Rate.

A digital image is a numerical representation of visual perception that can be manipulated according to specifications. In order to reduce the cost of storage and transmission, digital images are compressed. Depending upon the quality of reconstruction, compression methods are categorized as Lossy and Lossless compression. The lossless image compression techniques, where the exact recovery of data is possible, is the most challenging task considering the tradeoff between the compression ratio achieved and the quality of reconstruction. The inherent data redundancies like interpixel redundancy and coding redundancy in the image are exploited for this purpose. The interpixel redundancy is treated by decorrelation using Run-length Encoding, Predictive Coding, and other Transformation Coding techniques. While entropy-based coding can be achieved using Huffman codes, arithmetic codes, and the LZW algorithm, which eliminates the coding redundancy. During the implementation of these sequential coding algorithms, the direction used for data scanning plays an important role. A study of various image compression techniques using sequential coding schemes is presented in this paper. The experimentation on 100 gray-level images comprising 10 different classes is carried out to understand the effect of the direction of scanning of data on its compressibility. Depending upon this study the interrelation between the maximum length of the Run and compression achieved similarly the resultant number of Tuples and compression achieved is reported. Considering the fuzzy nature of these relations, fuzzy composition operations like max-min, min-max, and max-mean compositions are used for decision-making. In this way, a rational comment on which data scanning direction is suitable for a particular class of images is made in the conclusion.

A novel approach for lossless as well as lossy compression of monochrome images using Boolean minimization is proposed. The image is split into bit planes. Each bit plane is divided into windows or blocks of variable size. Each block is transformed into a Boolean switching function in cubical form, treating the pixel values as output of the function. Compression is performed by minimizing these switching functions using ESPRESSO, a cube based two level function minimizer. The minimized cubes are encoded using a code set which satisfies the prefix property. Our technique of lossless compression involves linear prediction as a preprocessing step and has compression ratio comparable to that of JPEG lossless compression technique. Our lossy compression technique involves reducing the number of bit planes as a preprocessing step which incurs minimal loss in the information of the image. The bit planes that remain after preprocessing are compressed using our lossless compression technique based on Boolean minimization. Qualitatively one cannot visually distinguish between the original image and the lossy image and the value of mean square error is kept low. For mean square error value close to that of JPEG lossy compression technique, our method gives better compression ratio. The compression scheme is relatively slower while the decompression time is comparable to that of JPEG.

Medical applications create an enormous amount of data. Medical data transmission via networks necessitates a huge bandwidth rate. Also, digital medical data necessitate enormous storage and archive. With the evolution of the Internet and multimedia designs, medical data is required to be transmitted in a rapid manner. One of the practical solutions to this issue is medical data compression. Data compression (DC) and transmission is important in the medical field. DC is used to transmit a large amount of data for minimizing the cost. DC is introduced to minimize the image for focusing on the removal of redundant data. DC is classified into two categories, namely, lossy and lossless techniques. DC is designed to reduce storage, bandwidth, and time consumed for transmission. Coding is utilized to remove unwanted data. The different DC algorithm is used to enhance the compression rate. Some of the medical data compression techniques are outlined to lessen data redundancy via specialized data coding and, as a result, can significantly minimize the constructive amount of medical data. In other words, medical data compression involves the procedure of encoding medical data in such a manner that less storage is essential to archive them over a network. The contemporary prototype of medical data compression is split into two stages, namely, designing and entropy coding. Selecting the appropriate prototype is paramount due to the reason that the more consistencies we identify, the more are the probabilities to minimize the series scope. Next, based on the understanding acquired via designing, unwanted data are eliminated by applying coding. Here, encoding is performed to eliminate dispensable data. As several DC techniques have been progressed, a requirement comes to light to assess the techniques, and an endeavor is made to review and classify different DC techniques based on three classifications, namely, coding schemes, data quality specifications, and application appropriateness. Some of the coding schemes for lossless data compression, to name a few, are run-length encoding, Huffman encoding, and LZW encoding. Also with the expeditious rise in high-speed data acquisition, bandwidth acquisition and storage have become the focal restrictions concerning DC techniques. We observed that it is impracticable to outline an exclusive lossless compression technique for different data types without a certain understanding of the series. It is also unfeasible to develop a disparate lossless compression algorithm for every potential series. The intelligent alternative is to devise comprehensive DC and to utilize such an algorithm to the series that can be handled, with a higher amount of precision. Some of the analyzed algorithms are component analysis, partial matching, state-space transitions, and tree sequence.

Visual Cryptography is a special encryption technique to hide information in images in such a way that it can be decrypted by the human vision if the correct key image is used. In Visual Cryptography the reconstructed image after decryption process encounters a major problem of Pixel expansion. This is overcome in this proposed method by minimizing the memory size using lossless image compression techniques. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages. Hybrid techniques are used in this proposed method as it can exploit multiple kinds of redundant information Keywords: Visual Cryptography; HVS; Image Compression; Vector Quantization; Run Length Encoding; Huffman Coding

Digital imaging in endoscopy

Digital image processing has become ubiquitous in our daily life and the demands to produce and process images are ever increasing. Large amounts of space are required to store these images. Image compression techniques are in high demand as they allow reduction in this storage space. The basis for image compression, as is for most other compression techniques, is to remove redundant and unimportant data. Lossless image compression techniques retain the original information in compact form and do not introduce any errors when decompressed. In this paper, we discuss such a lossless technique using a data structure that we name ldquogeneric Peano pattern mask treerdquo. It is an improvement over a previously discussed Lossless Image compression technique - ldquoPeano pattern mask treerdquo. Both these structures are based on the data structure - Peano mask tree.

Image Compression techniques have become a very important subject with the rapid growth of multimedia application. The main motivations behind the image compression are for the efficient and lossless transmission as well as for storage of digital data. Image Compression techniques are of two types; Lossless and Lossy compression techniques. Lossy compression techniques are applied for the natural images as minor loss of the data are acceptable. Entropy encoding is the lossless compression scheme that is independent with particular features of the media as it has its own unique codes and symbols. Huffman coding is an entropy coding approach for efficient transmission of data. This paper highlights the fractal image compression method based on the fractal features and searching and finding the best replacement blocks for the original image. Canonical Huffman coding which provides good fractal compression than arithmetic coding is used in this paper. The result obtained depicts that Canonical Huffman coding based fractal compression technique increases the speed of the compression and has better PNSR as well as better compression ratio than standard Huffman coding.

Due to the quick development of multimedia technology, now can see the increasing amount of multimedia data that is being generated or transmitted over the internet. digital image is one form of multimedia data, that can take different formats and different sizes. And here comes the main objective of image compression in transmitting or storing the data in an effective manner by decreasing the redundant or irrelevant information without significant loss of visual quality in image data. Compression of the images is utilized in various services, including TV broadcasts, satellite imagery, martial communications, webinars, medical imaging, weather reporting, etc. This paper provides a survey of the main techniques in image compression, covering both lossy & lossless approaches. The techniques that depend on lossy compression lose some of the image details during compression, while lossless compression techniques keep the image information without losing. Vector and scalar Quantization, transform coding, block truncation Coding, etc. are all examples of lossy approaches. Run length coding, entropy encoding, statistical coding, etc. are all examples of lossless techniques. This paper will assist the researchers in learning about these techniques and choosing appropriate techniques for their work.

Research at the Space Dynamics Laboratory(SDL) / Utah State University in image compression has resulted in the development of a hybrid compression algorithm for space related images. The basic algorithm is a hybrid of lossy and lossless compression techniques that have been implemented in hardware [I]. The lossy encoder uses Mean Removed Vector Quantization(MRVQ). The Lossless compressor is an entropy encoder used to encode the errors introduced by the lossy MRVQ. A constant bit rate is maintained by thresholding the MRVQ errors based on the number of previously encoded bits. This paper compares Huffman and Rice Compression techniques to be used in the lossless stage of the hybrid compressor. These method have been selected because hardware to implement them already exist. A single chip implementation of the Rice algorithm is available that processes at the rates required[2].

An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS), as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery, while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image, thus facilitating accurate diagnosis, of course at the expense of higher bit rates, i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization. Wavelet coding, neural networks, and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1, or even more), they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image, but the achievable compression ratios are only of the order 2:1, up to 4:1. In our paper, we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time, we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance, so that all the low rate codes are included at the beginning of the bit stream. Typically, the encoding process stops when the target bit rate is met. Similarly, the decoder can interrupt the decoding process at any point in the bit stream, and still reconstruct the image. Therefore, a compression scheme generating an embedded code can start sending over the network the coarser version of the image first, and continues with the progressive transmission of the refinement details. Experimental results show that our method can get a perfect performance in compression ratio and reconstructive image.