ABSTRACT: are usually tested in HEAD TO HEAD

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ABSTRACT: are usually tested in HEAD TO HEAD

ABSTRACT: Image compression is an implementation of the data compression. It will encode actual image with some bits. The purpose of this image compression is to decrease the redundancy. And the important purpose is to decrease an irrelevance of image data to be capable to record the data or send the data in an effective form. The original image can be perfectly recovered using the lossless compression techniques. They are also known as entropy coding, noiseless compression etc.the exact original data can be recovered. It was specified into two terms are efficiency and complexity. The image and they are using statistics or decomposition techniques to reduce the redundancy. The process that will effectively reduce the total number of bits needed to process certain information by using some different codings.the lossless will required to reproduce exactly when get decompressed again.AIM: The lossless compression is basically used to compressing the data that is when get decompressed. It is also known as a entropy coding as it uses the techniques of decomposing or statistics to remove or reduce the redundancy.KEYWORD: Huffman encoding, run length encoding, variable length encoding, LZW encoding, arithmetic encoding.INTRODUCTION: Lossless image compression means that you reduce the size of a one image without any quality loss. It is used to reduce the unnecessary mete data from JPEG and PNG files. There are some image formats are considered to be “lossless “such as GIF,PNG,BMP. The big benefit of lossless image compression is that it is allows you to retain the quality of your images while reducing their file size. The original data will be identical. It was used in many applications, such as ZIP file format and in the GNU tool gzip. The lossless compression algorithms and its implementation are usually tested in HEAD TO HEAD benchmarks. There a number of bench marks compression we have in lossless compression. Some of the bench marks cover only the data compression ratio. The lossless data compression algorithms cannot guarantee for all input data set. Each file is represented by string of bit of some arbitrary length. a compression algorithm that transforms all files in an output file that is no longer than the original file. Every single bit of data that was originally in the file remains after the file is uncompressed. The graphics interchange file (GIF) is an image format used in the web that was provided lossless compression. In the lossless image compression is rewrites the data in original file in more efficient way. Image compression address the problem of reducing the amount of data required to represent a digital image with no significant loss of information.  HUFFMAN ENCODING: Huffman encoding is a form of statistical coding which attempts to reduce the amount of bits required to represent a string of symbols. It has the direct bearing on the length of its representation. The more probable the occurrence of a symbol is, the shorter will be its bit size representation. Huffman compression is a variable length coding system that assigns smaller codes for more frequently used characters and larger codes for less frequently used characters in order to reduce the size of files being compressed and transferred. The original image is reconstructed. The decompression is done by using Huffman Decoding. Read input character wise and left to the tree until last element is reached in the tree. Perform a traversal of tree to generate code table. This will determine code for each element of tree in the following way. The code for each symbol may be obtained by tracing a path to the symbol from the root of the tree. A 1 is assigned for a branch in one direction and a 0 is assigned for a branch in the other direction. A symbol which is reached by branching right twice, then left once may be represented by the pattern ‘110’. The figure below depicts codes for nodes of a sample tree For example:                         *                      /         (0) (1)                      /                  (10)(11)                      /               (110) (111)                                                        0   A1:0.4                                                10                1   A2:0.35                       110                                1  A3:0.2                                                0.6                    111                       11  A4:0.05                               0.25        The average length of the code is given by the average of the product of probability of the symbol and number of bits used to encode it.RUN LENGTH ENCODING:  Run length is a very easy and simple technique of data compression. In run length encoding, an individual channel matrices were retrieved and used for processing. Each group of such repetitions was then replaced by the pixel value and the frequency of occurrence. The run length encoding is less useful with image such as same color occur many time. An encoding technique performs a lossless compression of input images that is based on sequence of identical values. There will be too much long runs of white pixels and short runs of block pixels. It was provided an efficient compression of data, while the data with large number of runs or large number pixel contains same intensity value. The same data value occurs in many consecutive data elements are stored as a single data value and count in original run. This is most useful on data that contains many such run: for example the relatively simple graphic image such as icons, line drawings, and animation. a Run Length Encoded Bitmap, used to compress the Windows 3.x startup screen.For example:  The run-length encoding for image compression algorithm to the above scan line,(12W) (1B) (12W) (3B) (24W) (1B) (14W).12W, means 12 count of white color pixel, and so on.Sample processing example:Input stream: 22 22 22 57 57 57 33 33 33 33 33 22Output stream: 322 457 533 22The output stream produce a series of frequency-pixel value pairs as previously discussed.  VARIABLE- LENGTH ENCODING: Sort the symbols according to the frequency count of their Occurrences. Recursively divide the symbols into two parts, each with approximately the same number of counts, until all parts contain only one symbol. This is in contrast to fixed length coding methods, for which data compression is only possible for large blocks of data.For example:Symbol H e l l oCount 1 1 2 1Frequency count of the symbols in “HELLO”.Variable-length codes can allow sources to be compressed and decompressed with zero error (lossless data compression) and still be read back symbol by symbol. The mapping M1={a?0,b?0,c?1}is not non-singular.The mappingM2={a?1,b?011,c?01110,d?1110,e?10011,f?0}is non-singular ; its extension will generate a lossless coding, which will be useful for general data transmission (but this feature is not always required). Note that it is not necessary for the non-singular code to be more compact than the source. LZW ENCODING: A lossless compression algorithm for digital data of many kinds, named for the creators Abraham Lempel and Jacob Ziv, and a later contributor, Terry Welch. LZW is based on a translation table that maps strings of input characters into codes. Through its incorporation in the graphics file formats GIF_89a and TIFF_LZW, LZW has come to be strongly associated with image compression. it is also used in GIF image files. The LZW method achieves compression by using codes 256 through 4095 to represent sequences of bytes. For example, code 523 may represent the sequence of three bytes: 231 124 234. Each time the compression algorithm encounters this sequence in the input file, code 523 is placed in the encoded file. During uncompressing, code 523 is translated via the code table to recreate the true 3 byte sequence. The longer the sequence assigned to a single code, and the more often the sequence is repeated, the higher the compression achieved. LZW also performs well when presented with extremely redundant data files, such as tabulated numbers, computer source code. LZW encoding is working based on the occurrence multiplicity of bit sequences in the pixel to be encoded. LZW compression works by replacing strings of characters with single codes without doing any analysis of the incoming text data. LZW is an adaptive technique. As the compression algorithm runs, a changing dictionary of the strings that have appeared in the text so far is maintained.ARITHMETIC ENCODING:  A new lossless image compression algorithm based on Arithmetic Coding.  Algorithms are selected appropriately for each pixel position. One of a large number of possible, dynamic, probability distributions, and encodes the current pixel prediction error by using this distribution as the model for the arithmetic encoder. We have experimentally compared our algorithm with Lossless JPEG, that is currently the lossless image compression standard, and also with FELICS and other lossless compression algorithms. Arithmetic coding differs from other forms of entropy encoding, such as Huffman coding, in that rather than separating the input into component symbols and replacing each with a code, arithmetic coding encodes the entire message into a single number, an arbitrary-precision fraction q where 0.0 ? q < 1.0. rithmetic coding could be implemented by a simple change of base, or radix. arithmetic coding may be interpreted as a generalized change of radix. For example, we may look at any sequence of symbols:As a number in a certain base presuming that the involved symbols form an ordered set and each symbol in the ordered set denotes a sequential integer A = 0, B = 1, C = 2, D = 3, and so on. This results in the following frequencies and cumulative frequencies. Symbol Frequency of occurrence Cumulative frequencyA 1 0B 2 1          D               3            3 We will convert DABDDB into a base-6 numeral, because 6 is the length of the string. The string is first mapped into the digit string 301331, which then maps to an integer by the polynomial:Remainder Identification Identified symbol Corrected remainder25100 25100 / 65 = 3 D (25100 ? 65 × 3) / 3 = 590590 590 / 64 = 0 A (590 ? 64 × 0) / 1 = 590590 590 / 63 = 2 B (590 ? 63 × 1) / 2 = 187187 187 / 62 = 5 D (187 ? 62 × 3) / 3 = 26?26 26 / 61 = 4 D (26 ? 61 × 3) / 3 = 2??2 2 / 60 = 2 B — ADVANTAGES OF LOSSLESS IMAGE COMPRESSION:1. Lossless compression algorithms reduce file size with no loss in image quality.2. Format of image has been in use since long time and is extremely portable.3. JPEG format can be used to store high resolution fast moving images which would be blurring in other image formats because owing to their small size, JPEG images can be stored quickly from a camera to storage device.4. Format of image is compatible with most of the hardware devices e.g. printers etc; therefore it is very easy to print the images in JPEG format.5. Format of image is compatible with almost every image processing application.DISADVANTAGES OF LOSSLESS IMAGE COMPRESSION:1. Quality of Image is reduced after compression owing to the loss of actual content of the image.2. Lossless image compression is not suitable for images with sharp edges and lines. JPEG image format is not capable of handling animated graphic images.3. JPEG images do not support layered images. Graphic designer need to work on layered images in order to manipulate and edit graphic images which is not possible with JPEG images.CONCLUSION: Image compression techniques will have the various compressions. The lossless compression will reduce the file size with no loss in one image. The original data will be identical. The purpose of this image compression is to decrease the redundancy. And the important purpose is to decrease an irrelevance of image data to be capable to record the data or send the data in an effective form. The lossless image compression is rewrites the data in original file in more efficient way.REFERENCES:1. Rafael C. Gonzalez and Richard E. Woods, "Digital Image Processing" Prentice Hall ofIndia, 2 nd Edition 20062. Shilpa Ijmulwar, Deepak Kapgate, "Lossless Image Compression Techniques andAlgorithms" IJCAT - Volume 1, Issue 9, October 20143. Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins, "Digital Image Processingusing MATLAB", 2 nd Edition 2010.4. Athira B. Kaimal, S. Manimurugan, C.S.C.Devadass, " Image Compression Techniques",International Journal of Engineering Inventions, Volume 2, Issue 4 (February 2013)5. M. Hemalatha, S. Nithya, " A Thorough Survey on Lossy Image CompressionTechniques", International Journal of Applied Engineering Research ISSN 0973-4562Volume 11, November 5 (2016)6. Malwinder Kaur, Navdeep Kaur,, "A LITREATURE SURVEY ON LOSSLESS IMAGECOMPRESSION", International Journal of Advanced Research in Computer andCommunication Engineering Vol. 4, Issue 3, March 2015.7. Manjit Sandhu, Jaipreet Kaur, Sukhdeep Kaur," Matlab Based Image Compression UsingVarious Algorithms",International Journal of Advanced Research in Computer Scienceand Software Engineering, Volume 6, Issue 4, April 2016.          

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