Image Compression: A Survey <s> INTRODUCTION <s> High data quality at low bit rate is an essential goal that people want to achieve. It is necessary to transfer data at low bit rate so that the bandwidth of the medium can be utilized efficiently. In most of the speech coding techniques the goal of low bit rate transfer is achieved but the data quality is affected badly. The proposed technique is an attempt to improve the data quality at low bit rate as well as fast transmission of data. The proposed technique protects the data quality by applying Linear Predictive Coding-10 and achieves the low bit rate by applying Quadrature Mirror Filter. A comprehensive analysis is on the basis of given parameters as size, compression time, Signal to Noise Ratio, power, energy, power in air, energy in air, mean, standard deviation and intensity. <s> BIB001 </s> Image Compression: A Survey <s> INTRODUCTION <s> Space research organizations, hospitals and military air surveillance activities, among others, produce a huge amount of data in the form of images hence a large storage space is required to record this information. In hospitals, data produced during medical examination is in the form of a sequence of images and are very much correlated; because these images have great importance, some kind of lossless image compression technique is needed. Moreover, these images are often required to be transmitted over the network. Since the availability of storage and bandwidth is limited, a compression technique is required to reduce the number of bits to store these images and take less time to transmit them over the network. For this purpose, there are many state-of the-art lossless image compression algorithms like CALIC, LOCO-I, JPEG-LS, JPEG20000; Nevertheless, these compression algorithms take only a single file to compress and cannot exploit the correlation among the sequence frames of MRI or CE images. To exploit the correlation, a new algorithm is proposed in this paper. The primary goals of the proposed compression method are to minimize the memory resource during storage of compressed data as well as minimize the bandwidth requirement during transmission of compressed data. For achieving these goals, the proposed compression method combines the single image compression technique called super spatial structure prediction with inter-frame coding to acquire grater compression ratio. An efficient compression method requires elimination of redundancy of data during compression; therefore, for elimination of redundancy of data, initially, the super spatial structure prediction algorithm is applied with the fast block matching approach and later Huffman coding is applied for reducing the number of bits required for transmitting and storing single pixel value. Also, to speed up the block-matching process during motion estimation, the proposed method compares those blocks that have identical sum and leave the others; therefore, the time taken by the block-matching process is reduced by minimizing the unnecessary overhead during the blockmatching process. Thus, in the proposed fast lossless compression method for medical image sequences, the twostage redundant data elimination process ultimately reduces the memory resource required for storing and transmission. The method is tested on the sequences of MRI and CE images and produces an improved compression rate. <s> BIB002 </s> Image Compression: A Survey <s> INTRODUCTION <s> Brain image compression is known as a subfield of image compression. It allows the deep analysis and measurements of brain images in different modes. Brain images are compressed to analyze and diagnose in an effective manner while reducing the image storage space. This survey study describes the different existing techniques regarding brain image compression. The techniques come under different categories. The study also discusses these categories. <s> BIB003
An approach of reducing the volume of graphics file in bytes without influenced image quality to unacceptable level. This minimization in size enables more images storage in an available memory space and cut down the transmission duration that is demanded by an image to be downloaded over the Internet. Compression can be classified in to two types Lossy and Lossless compression BIB002 BIB001 technique. In Lossless compression there is no information loss and the image also retains its quality it can be remodeled exactly the same as the original. Main application is Medical imagery BIB003 . In Lossy compression loss and missed information is bearable. Application is commercial distribution (DVD). Lossless methods cannot provide enough compression ratios. In this study, a review of various lossy and lossless algorithms ( Fig. 1) used so for image compression are explained, this will be helpful for researchers for further research by considering the existing work.