IMAGE COMPRESSION. Chapter  5 : (Basic)


 Alfred Gaines
 11 months ago
 Views:
Transcription
1 Chapter  5 : IMAGE COMPRESSION (Basic) Q() Explain the different types of redundncies that exists in image.? (8M May6 Comp) [8M, MAY 7, ETRX] A common characteristic of most images is that the neighboring pixels are correlated and therefore contain redundant information. The foremost task then is to find less correlated representation of the image. Two fundamental components of compression are redundancy and irrelevancy reduction. Redundancy reduction aims at removing duplication from the signal source (image/video). Irrelevancy reduction omits parts of the signal that will not be noticed by the signal receiver, namely the Human Visual System (HVS). Types of redundancies are as follows : Spatial Redundancy or correlation between neighbouring pixel values. Spectral Redundancy or correlation between different color planes or spectral bands. Temporal Redundancy or correlation between adjacent frames in a sequence of images (in video applications). Image compression research aims at reducing the number of bits needed to represent an image by removing the spatial and spectral redundancies as much as possible. In digital image compression, three basic data redundancies can be identified and exploited : Coding redundancy, Interpixel redundancy and Psychovisual redundancy. ) Coding Redundancy. If the gray levels of an image are coded in a way that uses more code symbols than absolutely necessary to represent each gray level then, the resulting image is said to contain coding redundancy. IP Help Line :
2 2) Interpixel Redundancy. The gray levels not equally probable. The value of any given pixel can be predicted from the value of its neighbors that is they are highly correlated. The information carried by individual pixel is relatively small. To reduce the interpixel redundancy the difference between adjacent pixels can be used to represent an image. 3) Psychovisual Redundancy. The psychovisual redundancies exist because human perception does not involve quantitative analysis of every pixel or luminance value in the image. It s elimination is real visual information is possible only because the information itself is not essential for normal visual processing. Q(2) Describe the general compression system model. (6M May6 IT) Image compression system reduces the number of bits needed to represent an image by removing the spatial and spectral redundancies as much as possible. In digital image compression, three basic data redundancies can be identified and exploited. Coding redundancy, Interpixel redundancy and psychovisual redundancy. Input Image Preprocessing Lossless Encoding Entropy Encoder Encoded Image Lossy Quantization IP Help Line :
3 Source Encoder (or Linear Transformer) Over the years, a variety of linear transforms have been developed which include Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) [], Discrete Wavelet Transform (DWT) and many more, each with its own advantages and disadvantages. Quantizer A quantizer simply reduces the number of bits needed to store the transformed coefficients by reducing the precision of those values. Since this is a manytoone mapping, it is a lossy process and is the main source of compression in an encoder. Quantization can be performed on each individual coefficient, which is known as Scalar Quantization (SQ). Quantization can also be performed on a group of coefficients together, and this is known as Vector Quantization (VQ). Both uniform and nonuniform quantizers can be used depending on the problem at hand. Entropy Encoder An entropy encoder further compresses the quantized values losslessly to give better overall compression. It uses a model to accurately determine the probabilities for each quantized value and produces an appropriate code based on these probabilities so that the resultant output code stream will be smaller than the input stream. The most commonly used entropy encoders are the Huffman encoder and the arithmetic encoder, although for applications requiring fast execution, simple runlength encoding (RLE) has proven very effective. It is important to note that a properly designed quantizer and entropy encoder are absolutely necessary along with optimum signal transformation to get the best possible compression. IP Help Line :
4 Q(3) Write short notes on: Psychovisual redundancy. (M Dec6 IT) (5M May5 Comp) Eye does not respond with equal sensitivity to all visual information. Certain information has lees relative importance than other information in normal visual processing. This information is said to be psychovisually redundant. It can be eliminated without significantly impairing the quality of image perception. The psychovisual redundancies exist because human perception does not involve quantitative analysis of every pixel or luminance value in the image. In general, an observer searches for distinguishing features such as edges or textural regions and mentally combines them into recognizable groupings. The brain then correlates these groupings with prior knowledge in order to complete the image interpretation processes. Psychovisual redundancies are associated with real visual information. It s elimination in real visual information is possible only because the information itself is not essential for normal visual processing. Since the elimination of psychovisual redundant data, results in a loss of quantitative information, its is commonly referred to as quantization Q(4) Classify Image Compression Techniques. (5M Dec6 Comp) In Lossy compression techniques reconstructed image contains loss of information. Which in turn produces distortion in the image. The resulting distortion may or may not be visually apparent. This compromising accuracy of the reconstructed image gives very high compression ratio. IP Help Line :
5 Q(5) Explain Objective And Subjective Fidelity Criteria (M Dec 4 Comp) The criteria for an assessment of a the quality of an image are ) Objective Fidelity Criteria and 2) Subjective Fidelity Criteria. ) Objective Fidelity Criteria :  a) Mean Square Error [MSE] : Let f (x,y) represent an input image and Let fˆ (x, y) denote an estimate or appro of f (x,y) For any value of x and y, the error e (x,y) between f(x,y) and fˆ (x, y) can be defined as, e (x,y) = f (x,y) fˆ (x, y) So that the total error between the two images is M N x, y= [ f ( x, y) fˆ ( x, y) ] Where the images are of size M x N. The root mean square error between f (x,y) and fˆ (x, y) is defined as, M 2 ˆ 2 = N e rms [ f ( x, y) f ( x, y) ] == MSE MN x= y= b) Signal to Noise Ratio [SNR} SNR c) Peak Signal to Noise Ratio [PSNR] IP Help Line : = M x =, PSNR M N x =, y = N y = [ f = M x=, f ( x, y) ( x, y) N y= M N x=, y= 2 fˆ ( x, y)] (255)2 [ f ( x, y) 2 fˆ ( x, y) ] 2) Subjective fidelity criteria : Images are viewed by human beings. Therefore measuring image quality by the subjective evaluations of a human observer is more appropriate. This can be accomplished by showing a typical decompressed image to an appropriate cross section of viewers and averaging their evaluations. The evaluations may be made using an absolute rating scale or by means of side by side comparisons of f (x,y) and fˆ (x, y), side by side comparisons can be done with the following scale. 2
6 ) {, 2, 3, 4, 5, 6} to represent subjective evaluations such as {Excellent, Fine, Passable, Marginal, Inferior, Unusable} respectively. 2) { 3,  2, ,,, 2, 3} to represent subjective evaluations such as {much worse, worse, slightly worse, the same, slightly better, better, much better] respectively. These evaluations are said to be based on subjective fidelity criteria. Value Rating Description Excellent An image of extremely high quality, as good as you could desire. 2 Fine An image of high quality, providing enjoyable viewing. Interference is not objectionable. 3 Passable An image of acceptable quality. Interference is not objectionable. 4 Marginal An image of poor quality; you wish you could improve it. Interference is somewhat objectionable. 5 Inferior A very poor image, but you could watch it. Objectionable interference is definitely present. 6 Unusable An image so bad that you could not watch it. Q(6) Explain Arithmetic Coding (6M Dec 4 Comp) In arithmetic coding one to one correspondence between source symbols and code words does not exists. Instead an entire sequence is assigned single code word. The codeword its self defines an interval of real numbers between and. As the number of symbols in the message increases, the interval used to represent it becomes smaller and the number of information bits required to represent the interval becomes larger. Each symbol of the message reduces the size of the interval in accordance with its probability of occurrences Example :  IP Help Line :
7 Q(7) Classify with reasons, the following data compression techniques into Lossy and Lossless schemes [8 M, MAY 7, ETRX] i] Run length coding. ii] DCT compression. (Lossless) Q(8) Given below is a table of eight symbol and their frequency of occurrence. Symbol S S S2 S3 S4 S5 S S7 Frequency (a) Give Huffman code for each eight symbol. (6M Dec.4 I.T) (b) Evaluate minimum number of average bits of sequence per symbol. (c) What is coding efficiency for the code you have obtained in part (a)? Q(9) Generate Huffman code for the given image source. Calculate entropy of the source, average length of the code generated and the compression ratio achieved compare it to standard binary encoding. (2M May6 Etrx) Symbol A A2 A3 A4 A5 A6 A7 A8 probability Q() Find Huffman code for the following six symbols. The symbols and their probabilities are given in tabular form: (M Dec6 IT) Symbol A A2 A3 A4 A5 A6 Probability Q() Short Note: Huffman coding and Run length coding. (M Dec5 IT) Q(2) How many unique Huffman codes are there for three symbol source? construct these modes. [4 M, MAY 7, Comp] Solution : Consider three symbols s, s2, s3 with probabilities p,p2 and p3 respectively. IP Help Line :
8 Case : When P > P 2 > P 3 SYMBOL PROBABILTY S P P23 S2 P2 P S3 SYMBOL S S2 S3 CODE Case 2: When P > P 3 > P 2 SYMBOL PROBABILTY S P P23 S3 P3 P S2 P2 SYMBOL S S3 S2 CODE Case 3: When P 2 > P > P 3 SYMBOL PROBABILTY S2 P2 P23 S P P S3 P3 SYMBOL S2 S S3 CODE Case 4: When P 2 > P 3 > P SYMBOL PROBABILTY S2 P2 P23 S3 P3 P S P SYMBOL S2 S3 S CODE IP Help Line :
9 . Case 5: When P 3 > P > P 2 SYMBOL PROBABILTY S3 P3 P23 S P P S2 P2 SYMBOL S3 S S2 CODE Case 6: When P 3 > P 2 > P SYMBOL PROBABILTY S3 P3 P23 S2 P2 P S P SYMBOL S3 S2 S CODE Q(3) For a given source A = { a, a2, a3, a4 } the following codes were developed. Check for each of them whether it is uniquely decodable or not. Also state which is the most optimum compared to others? Why? Solution : SYMBOL PROBABILITY CODE CODE2 CODE3 CODE4 a.5 a2.25 a3.25 a4.25 Code 2 and Code 4 are NOT uniquely decodable code. In Code : The prefix of the code is not a valid codeword. Therefore Code is uniquely decodable Code. In Code2, the codeword for symbol a4 is. The single bit prefix of code(a4) is which is a valid codeword. Therefore, code2 is NOT uniquely decodable code. In Code3, the single bit prefix of a2, a3 and a4 is and it is not a valid codeword. Two bit prefix of code(a3) and Code(a4) is and it is not a valid codeword. Therefore Code3 is uniquely decodable Code. IP Help Line :
10 In Code4, the single bit prefix is not a valid codeword. Two bit prefix of Code(a3) is (which is not a valid codeword.) and Code4 is ( which is a valid codeword). Therefore, code4 is NOT uniquely decodable code. To find Optimum Code, find Average Length. () For Code : Lavg = N k = Pk Lk = P L + P 2 L 2 + P 3 L 3 + P 4 L 4 = (.5)(2)+ (.25)(2)+ (.25)(2)+ (.25)(2) Lavg = bit/symbol (2) For Code3 : Lavg = N k = Pk Lk = P L + P 2 L 2 + P 3 L 3 + P 4 L 4 = (.5)()+ (.25)(2)+ (.25)(3)+ (.25)(3) Lavg =.75 bit/symbol Code  has minimum value of Average Length. So the efficiency of Code is greater than the efficiency of Code3. Therefore Code is an Optimum code. Q(4) Can variable length coding procedures be used to compress a histogram equalized image with gray levels?. Explain. (M May6 Comp) Q(5) What is Constant Area Coding Technique? (5M Dec6 Comp) The image is divided into blocks of size (m x n pixels which are classified as all white, all black or mixed intensity. the most frequently occurs category is assigned the bit code word and other two categories are assigned the 2 codes and. The mixed intensity category block is coded using 2 bit code word as a prefix which is followed by the m x n bit pattern of the pixel. Example : IP Help Line :
11 Compression is achieved because the (m x n) bits that will be replaced by bit or 2 code words. When predominantly white text documents are compressed a slightly simpler approach is to code the solid white areas as and all other blocks (including solid black blocks) by a followed by the bit pattern of the block. This approach is called white block skipping (WBS). As few black areas are expected, they are grouped with the mixed intensity regions. Q(6) Explain Bit plane Coding. (5M May5 Comp)(6M Dec 4 Comp) Bit plane technique is an error free compression that exploits the images interpixel redundancies. Bit plane technique is based on the concept of decomposing a multilevel image (monocrome or colour) into a series of binary images and compressing each binary image. Bit Plane Decomposition : The gray levels of an m bit gray scale image can be represented in the form of the base 2 polynomial. am 2 m a 2 + a 2 By separating the m coefficients into m bit planes, the image gets decomposed into bit planes. Zero order bit plane is generated by collecting a bit of each pixel. In General, each bit plane is numbered from to (m ) and is constructed by setting its pixel equal to the values of the appropriate bits from each pixel. The disadvantage of this approach is that small changes in gray level can have a significant impact on the complexity of the bit planes. If a pixel of intensity 27 () is adjacent to a pixel of intensity 28 ( ), every bit plane will contain a corresponding to (OR to ) transition. This problem can be solved by representing the image by m bit Gray code. The m bit gray code gm.. g2 g g can be computed from IP Help Line :
12 gi = ai + ai + i m 2 gm = am This code has the unique property that successive code words differ in only one bit position. Thus small changes in gray level are lees likely to affect all m bit planes. When gray levels 27 and 28 are adjacent the gray code of 27 = and 28 = That means only the 7 th bit plane will contain a to transition Q(7) Explain B2 Code : B2 Code is variable length coding. Each code word is made up of continuation bits denoted as C and information bits which are natural binary numbers. The purpose of the continuation code is to separate the individual code words. In B2 code, two information bits are used per continuation bits and hence B2 code. Example : Consider six symbols with probabilities as given below, Symbol Probability B2 Code S.4 C S2.3 C S3. C S4. C S5.6 CC S6.4 CC Message : S S2 S3 S5 S S6 B2 code : C C C C C C C C C= C= C= C= C= C= C= C= IP Help Line :
13 To Average Length : Lavg = N k = Pk Lk = P L + P 2 L 2 + P 3 L 3 + P 4 L 4 = (.4)(3) + (.3)(3) + (.)(3) + (.)(3) + (.6)(6) + (.4)(6) Lavg = 3.3 bit/symbol Q(8) Explain Shift codes. Algorithm to find shift code word. i) Arrange the source symbols so that their probabilities are monotonically decreasing. ii) Divide the total number of symbol into symbol blocks of equal size. iii) Add special shift up and / or shift down symbols to identify each block. Each Time a shift up or shift down symbol is recognized at the decoder, it moves one block up or down with respect to a predefined reference block. Example of Shift code is Binary Shift Code. Consider 2 source symbols, Arrange the source symbols so that their probabilities are monotonically decreasing and divide into three blocks of seven symbols. The individual symbols (a thro a 7 ) of upper block can be considered as the reference block. Code the symbols in reference block with the binary codes thro. The 8 th binary code is obtained by single shift up control symbol followed by. The 9 th binary code is obtained by single shift up control symbol followed by and so on... This procedure continues to find the code word of the next symbol. The code word for each symbol is control symbol followed by binary sequence of corresponding earlier block symbol codeword. IP Help Line :
14 Q(9) What do you mean by Variable Length Coding technique? Explain at least two Techniques. (M Dec6 Comp) Examples of Variable Length Coding are : Huffman Code, B2 Code, Shift Code. (Lossy) Q(2) Explain Improved Gray Scale (IGS) Quantization : IGS method recognizes the eye s inherent sensitivity to edges and breaks them up by adding to each a pseudo random number. The pseudo random number is generated from low order bits of neighbouring pixels. Because low order bits are fairly random, this amounts to adding a level of randomness which depends on the local characteristics of the image, to the artificial edges normally associated with false contouring. IGS Quantization Procedure : PIXEL GRAY LEVEL SUM IGS CODE N / A N / A SUM is initially set to Zero. Then Sum = Current 8 bit gray level + Four least significant bits of a previously generated sum. If the four most significant bits of the current value are instead of four least significant bits of a previously generated sum, are added. The four most significant bits of the resulting sum are used as the coded pixel value. IP Help Line :
15 Q(2) Consider an 8pixel line of gray scale data { 2,2,3,3,,3,57,54 }, which has been uniformly quantized with 6 bit accuracy. Construct its 3 bit IGS code. (8M May6 Etrx) Solution : Pixel Pixel in 6 bit Sum IGS Code Binary Q(22) DPCM/DCT encoder for video compression. (5M May6 I.T) Q(23) Write Short Notes on: (a) Compression using LZW method (M May6 Comp)(7M May6 Etrx) (b) Delta Modulation and differential pulse code modulation (DPCM). (6M May6 Comp) Q(24) TRUE OR FALSE AND JUSTIFY (a) All image compression technique are invertible. (4M May7 IT) (3M May6 Etrx) False All image compression techniques are not invertible. Lossless compression techniques such as Runlength encoding, Huffman coding, Arithmetic coding, CAC coding, B2 code DPCM are invertible. Lossy compression techniques such as JPEG, IGS Quantization are not invertible. (b) Runlength coding is loss less coding but may not give data compression always. [4 M, MAY 7, ETRX] True Runlength coding is a lossless compression coding technique. Runlength codeword consists of repeated sequence of tokens. Each token consists of two words : () pixel value (2) consecutive occurrence of pixel value. Runlegth Encoding (RLE) is effective compression technique when pixel value is repeated consecutively. In the overall image if the pixel value is not repeatedly consecutively large number of times, RLE cannot give compression. IP Help Line :
16 For Ex RLE coded image is =, 8, 3,, 2, 8, 3,, Size : bytes Image is compressed Size of input Image is 25 bytes For Ex RLE coded image is =, 5,,, 8, 3, 2, 2, 8, 3, 2,,, 2, 2,,, 5, 3,, 2,,,, 4, RLE coded imag Size : 28 bytes Size of input Image is 25 bytes Hence size of the RLE coded image is larger than size of input image. That means every time RLE does not gives compression. Q(25) Write Short Notes on Transform Coding In transform coding a reversible, linear transform is used to map the image into a set of transform coefficients which are then quantized and coded. For most images, a significant number of coefficients have small magnitudes and can be coarsely quantized (or discarded entirely) with little image distortion. The following fig. shows general block diagram of transform coding system. Input Compressed Forward Transform Quantizer Symbol encoder image Image (N x N) Compressed image Deccmpressed Symbol decoder Inverse Transform image IP Help Line :
17 The decoder implements the inverse sequence of steps. An N x N input image first is subdivided into subimages of size (n x n) which are then transformed to generate n x n subimage transform arrays. The goal of the transformation process is to decorrelate the pixel of each subimage or to pack as much information as possible into the smallest number of coefficients. Transform coding system based on DCT, WT, FT can be effectively applied. The choice of a particular transform in a given application depends on the amount of reconstruction error that can be tolerated and computational resources available. Q(26) Explain the basic principle of transform coding for image compression and illustrate same with the help of DFT and DCT. (M Dec5 IT) Q(27) Write short Note on : JPEG Compression IP Help Line :
18 JPEG ENCODER ( Sequential Baseline System) Input Image Data FDCT Quantizer Zigzag ordering Entropy Encoding Compressed Image 8 X 8 Blocks Quantization Table Specification Entropy Table Specification i) In the sequential baseline system, an input image is first divided into pixel blocks of size 8 x 8 which are processed left to right, top to bottom. ii) iii) iv) Each block of 8 x 8 pixel size is level shifted by subtracting the quantity 2 n where 2 n is the highest gray value (ie for n = 8, 2 n = 256). The 2D DCT of the block is then computed and quantized. c( u, v) Ô(u,v) = Round Where Q(u,v) is the quantization factor. Q( u, v) In DCT sequential baseline system, Input and output data precision is limited to 8 bits and DCT quantized values are restricted to bits. Quantized DCT coefficients are reordered using the zigzag pattern to form a D sequence of quantized coefficients. Zigzag reordering results increasing spatial frequency components with long runs of zeros. v) Nonzero AC coefficients are coded using a variable length code that defines the coefficients value and the number of preceding zeros. vi) DC coefficients are the difference coded relative to the Dc coefficient of the previous subimage. The output consists of sequence of tokens is repeated until the block is complete. IP Help Line :
19 ) Run length : Number of consecutive zeros that preceded the current element in the DCT output matrix. 2) Bit count : The number of bits to follow in the amplitude number. 3) Amplitude : The amplitude of the DCT coefficient. JPEG DECODER : Entropy Decoding Zigzag Reordering Dequantizer IDCT Compressed Image Entropy Table Specification Quantization Table Specification Reconstructed Image Data i) To decompress the image, decoder first creates the normalized transform coefficient. ii) After demoralization, the DCT coefficients are computed. C(u,v) = c(u,v) Q(u,v). iii) iv) The reconstructed subimage is obtained by taking the inverse DCT of the demoralized coefficients and then level shifting each pixel by 2 n. The difference between the original and reconstructed image is the result of lossy nature of compression and decompression process. v) The root mean square error of the overall compression and reconstruction process is approximately 5.9. IP Help Line :
20 Q(28) Write short note on : Image Compression Standards. (5M Dec 4 Comp) Image Compression Standards : Joint Photographic Expert Group (JPEG) has developed an international standard for general purpose colour still image compression standards. Motion Picture Expert Group(MPEG) has developed full motion video image sequence with application to digital video distribution and high Definition Television (HDTV) JPEG still Image Compression: The JPEG standard defines three difference coding systems.. Lossy baseline Coding Systemwhich is based on sequential DCT based compression. 2. An Extended Coding System, for greater compression, higher precision or progressive reconstructions applications. 3. Lossless independent coding system based on sequential predictive compression techniques. Q(29) Write short note on : Vidéo Compression Standards. (5M Dec 4 Comp) MPEG Full motion video compression: The MPEG standard defines different coding systems. Video teleconferencing standards. 2. Multimedia standards. H.263 standard defines low bit rate 3 kbps speed. H.32 standard supports ISDN Bandwidth. IP Help Line :
21 () MPEG standard is an Entertainment quality video compression standard for the storage and retrieval of compressed imaginary on digital media such as compact disk read only memory (CDRom). MPEG standard is written to allow higher bit rates and higher quality encoding. (2) MPEG II standard supports video transfer rates between 5 to Mbps a range which is suitable for cable TV distribution and narrowchannel satellite broadcasting. (3) MPEG IV standard are developed for small frame full motion compression with slow refresh rates. Both MPEG standards and H.26 standards extends the DCT based compression approach. IP Help Line :
IMAGE COMPRESSION. Image Compression. Why? Reducing transportation times Reducing file size. A two way event  compression and decompression
IMAGE COMPRESSION Image Compression Why? Reducing transportation times Reducing file size A two way event  compression and decompression 1 Compression categories Compression = Image coding Stillimage
More informationIMAGE COMPRESSION I. Week VIII Feb /25/2003 Image CompressionI 1
IMAGE COMPRESSION I Week VIII Feb 25 02/25/2003 Image CompressionI 1 Reading.. Chapter 8 Sections 8.1, 8.2 8.3 (selected topics) 8.4 (Huffman, runlength, lossless predictive) 8.5 (lossy predictive,
More information7.5 Dictionarybased Coding
7.5 Dictionarybased Coding LZW uses fixedlength code words to represent variablelength strings of symbols/characters that commonly occur together, e.g., words in English text LZW encoder and decoder
More informationCS 335 Graphics and Multimedia. Image Compression
CS 335 Graphics and Multimedia Image Compression CCITT Image Storage and Compression Group 3: Huffmantype encoding for binary (bilevel) data: FAX Group 4: Entropy encoding without error checks of group
More informationCoE4TN4 Image Processing. Chapter 8 Image Compression
CoE4TN4 Image Processing Chapter 8 Image Compression Image Compression Digital images: take huge amount of data Storage, processing and communications requirements might be impractical More efficient representation
More informationA NEW ENTROPY ENCODING ALGORITHM FOR IMAGE COMPRESSION USING DCT
A NEW ENTROPY ENCODING ALGORITHM FOR IMAGE COMPRESSION USING DCT D.Malarvizhi 1 Research Scholar Dept of Computer Science & Eng Alagappa University Karaikudi 630 003. Dr.K.Kuppusamy 2 Associate Professor
More informationCHAPTER 4 REVERSIBLE IMAGE WATERMARKING USING BIT PLANE CODING AND LIFTING WAVELET TRANSFORM
74 CHAPTER 4 REVERSIBLE IMAGE WATERMARKING USING BIT PLANE CODING AND LIFTING WAVELET TRANSFORM Many data embedding methods use procedures that in which the original image is distorted by quite a small
More information06/12/2017. Image compression. Image compression. Image compression. Image compression. Coding redundancy: image 1 has four gray levels
Theoretical size of a file representing a 5k x 4k colour photograph: 5000 x 4000 x 3 = 60 MB 1 min of UHD tv movie: 3840 x 2160 x 3 x 24 x 60 = 36 GB 1. Exploit coding redundancy 2. Exploit spatial and
More information15 Data Compression 2014/9/21. Objectives After studying this chapter, the student should be able to: 151 LOSSLESS COMPRESSION
15 Data Compression Data compression implies sending or storing a smaller number of bits. Although many methods are used for this purpose, in general these methods can be divided into two broad categories:
More informationDIGITAL IMAGE PROCESSING WRITTEN REPORT ADAPTIVE IMAGE COMPRESSION TECHNIQUES FOR WIRELESS MULTIMEDIA APPLICATIONS
DIGITAL IMAGE PROCESSING WRITTEN REPORT ADAPTIVE IMAGE COMPRESSION TECHNIQUES FOR WIRELESS MULTIMEDIA APPLICATIONS SUBMITTED BY: NAVEEN MATHEW FRANCIS #105249595 INTRODUCTION The advent of new technologies
More informationStatistical Image Compression using Fast Fourier Coefficients
Statistical Image Compression using Fast Fourier Coefficients M. Kanaka Reddy Research Scholar Dept.of Statistics Osmania University Hyderabad500007 V. V. Haragopal Professor Dept.of Statistics Osmania
More informationHYBRID TRANSFORMATION TECHNIQUE FOR IMAGE COMPRESSION
31 st July 01. Vol. 41 No. 00501 JATIT & LLS. All rights reserved. ISSN: 1998645 www.jatit.org EISSN: 18173195 HYBRID TRANSFORMATION TECHNIQUE FOR IMAGE COMPRESSION 1 SRIRAM.B, THIYAGARAJAN.S 1, Student,
More informationcompression and coding ii
compression and coding ii OleJohan Skrede 03.05.2017 INF2310  Digital Image Processing Department of Informatics The Faculty of Mathematics and Natural Sciences University of Oslo After original slides
More informationImage Compression Compression Fundamentals
Compression Fndamentals Data compression refers to the process of redcing the amont of data reqired to represent given qantity of information. Note that data and information are not the same. Data refers
More informationLossless Image Compression having Compression Ratio Higher than JPEG
Cloud Computing & Big Data 35 Lossless Image Compression having Compression Ratio Higher than JPEG Madan Singh madan.phdce@gmail.com, Vishal Chaudhary Computer Science and Engineering, Jaipur National
More informationImage Compression for Mobile Devices using Prediction and Direct Coding Approach
Image Compression for Mobile Devices using Prediction and Direct Coding Approach Joshua Rajah Devadason M.E. scholar, CIT Coimbatore, India Mr. T. Ramraj Assistant Professor, CIT Coimbatore, India Abstract
More informationIn the first part of our project report, published
Editor: Harrick Vin University of Texas at Austin Multimedia Broadcasting over the Internet: Part II Video Compression Borko Furht Florida Atlantic University Raymond Westwater Future Ware Jeffrey Ice
More informationVideo Codec Design Developing Image and Video Compression Systems
Video Codec Design Developing Image and Video Compression Systems Iain E. G. Richardson The Robert Gordon University, Aberdeen, UK JOHN WILEY & SONS, LTD Contents 1 Introduction l 1.1 Image and Video Compression
More informationIntroduction to Video Coding
Introduction to Video Coding o Motivation & Fundamentals o Principles of Video Coding o Coding Standards Special Thanks to Hans L. Cycon from FHTW Berlin for providing firsthand knowledge and much of
More informationFPGA IMPLEMENTATION OF BIT PLANE ENTROPY ENCODER FOR 3 D DWT BASED VIDEO COMPRESSION
FPGA IMPLEMENTATION OF BIT PLANE ENTROPY ENCODER FOR 3 D DWT BASED VIDEO COMPRESSION 1 GOPIKA G NAIR, 2 SABI S. 1 M. Tech. Scholar (Embedded Systems), ECE department, SBCE, Pattoor, Kerala, India, Email:
More informationAudio and video compression
Audio and video compression 4.1 introduction Unlike text and images, both audio and most video signals are continuously varying analog signals. Compression algorithms associated with digitized audio and
More informationCS 260: Seminar in Computer Science: Multimedia Networking
CS 260: Seminar in Computer Science: Multimedia Networking Jiasi Chen Lectures: MWF 4:105pm in CHASS http://www.cs.ucr.edu/~jiasi/teaching/cs260_spring17/ Multimedia is User perception Content creation
More informationPerceptual Coding. Lossless vs. lossy compression Perceptual models Selecting info to eliminate Quantization and entropy encoding
Perceptual Coding Lossless vs. lossy compression Perceptual models Selecting info to eliminate Quantization and entropy encoding Part II wrap up 6.082 Fall 2006 Perceptual Coding, Slide 1 Lossless vs.
More informationCompression; Error detection & correction
Compression; Error detection & correction compression: squeeze out redundancy to use less memory or use less network bandwidth encode the same information in fewer bits some bits carry no information some
More informationImage Coding and Data Compression
Image Coding and Data Compression Biomedical Images are of high spatial resolution and fine grayscale quantisiation Digital mammograms: 4,096x4,096 pixels with 12bit/pixel 32MB per image Volume data (CT
More informationSource Coding Basics and Speech Coding. Yao Wang Polytechnic University, Brooklyn, NY11201
Source Coding Basics and Speech Coding Yao Wang Polytechnic University, Brooklyn, NY1121 http://eeweb.poly.edu/~yao Outline Why do we need to compress speech signals Basic components in a source coding
More informationWhat is multimedia? Multimedia. Continuous media. Most common media types. Continuous media processing. Interactivity. What is multimedia?
Multimedia What is multimedia? Media types +Text + Graphics + Audio +Image +Video Interchange formats What is multimedia? Multimedia = many media User interaction = interactivity Script = time 1 2 Most
More informationRedundant Data Elimination for Image Compression and Internet Transmission using MATLAB
Redundant Data Elimination for Image Compression and Internet Transmission using MATLAB R. Challoo, I.P. Thota, and L. Challoo Texas A&M UniversityKingsville Kingsville, Texas 783638202, U.S.A. ABSTRACT
More informationThe Existing DCTBased JPEG Standard. Bernie Brower
The Existing DCTBased JPEG Standard 1 What Is JPEG? The JPEG (Joint Photographic Experts Group) committee, formed in 1986, has been chartered with the Digital compression and coding of continuoustone
More informationThe BestPerformance Digital Video Recorder JPEG2000 DVR V.S MPEG & MPEG4(H.264)
The BestPerformance Digital Video Recorder JPEG2000 DVR V.S MPEG & MPEG4(H.264) Many DVRs in the market But it takes brains to make the best product JPEG2000 The best picture quality in playback. Brief
More informationBitPlane Decomposition Steganography Using Wavelet Compressed Video
BitPlane Decomposition Steganography Using Wavelet Compressed Video Tomonori Furuta, Hideki Noda, Michiharu Niimi, Eiji Kawaguchi Kyushu Institute of Technology, Dept. of Electrical, Electronic and Computer
More informationMultimedia. What is multimedia? Media types. Interchange formats. + Text +Graphics +Audio +Image +Video. Petri Vuorimaa 1
Multimedia What is multimedia? Media types + Text +Graphics +Audio +Image +Video Interchange formats Petri Vuorimaa 1 What is multimedia? Multimedia = many media User interaction = interactivity Script
More informationAnatomy of a Video Codec
Anatomy of a Video Codec The inner workings of Ogg Theora Dr. Timothy B. Terriberry Outline Introduction Video Structure Motion Compensation The DCT Transform Quantization and Coding The Loop Filter Conclusion
More informationComputer Faults in JPEG Compression and Decompression Systems
Computer Faults in JPEG Compression and Decompression Systems A proposal submitted in partial fulfillment of the requirements for the qualifying exam. Cung Nguyen Electrical and Computer Engineering University
More informationMultimedia Signals and Systems Motion Picture Compression  MPEG
Multimedia Signals and Systems Motion Picture Compression  MPEG Kunio Takaya Electrical and Computer Engineering University of Saskatchewan March 9, 2008 MPEG video coding A simple introduction Dr. S.R.
More informationJPEG Compression Using MATLAB
JPEG Compression Using MATLAB Anurag, Sonia Rani M.Tech Student, HOD CSE CSE Department, ITS Bhiwani India ABSTRACT Creating, editing, and generating s in a very regular system today is a major priority.
More informationVideo Coding in H.26L
Royal Institute of Technology MASTER OF SCIENCE THESIS Video Coding in H.26L by Kristofer Dovstam April 2000 Work done at Ericsson Radio Systems AB, Kista, Sweden, Ericsson Research, Department of Audio
More informationΝΤUA. Τεχνολογία Πολυμέσων
ΝΤUA Τεχνολογία Πολυμέσων 3. Διάλεξη 3: Transform Coding Rate Distortion Theory D may be the Mean Square Error or some human perceived measure of distortion Types of Lossy Compression VBR Variable Bit
More informationAn Intraframe Coding by Modified DCT Transform using H.264 Standard
An Intraframe Coding by Modified DCT Transform using H.264 Standard C.Jilbha Starling Dept of ECE C.S.I Institute of technology Thovalai,India D.Minola Davids C. Dept of ECE. C.S.I Institute of technology
More informationChapter 11.3 MPEG2. MPEG2: For higher quality video at a bitrate of more than 4 Mbps Defined seven profiles aimed at different applications:
Chapter 11.3 MPEG2 MPEG2: For higher quality video at a bitrate of more than 4 Mbps Defined seven profiles aimed at different applications: Simple, Main, SNR scalable, Spatially scalable, High, 4:2:2,
More informationVideo Compression Using Spatial and Temporal Redundancy A Comparative Study
Video Compression Using Spatial and Temporal Redundancy A Comparative Study Kusuma.H.R 1, Dr.Mahesh Rao 2 P.G Student, Department of Electronics and Communication, MIT Mysore, Karnataka, India 1 Professor,
More informationA realtime SNR scalable transcoder for MPEG2 video streams
EINDHOVEN UNIVERSITY OF TECHNOLOGY Department of Mathematics and Computer Science A realtime SNR scalable transcoder for MPEG2 video streams by Mohammad Alkhrayshah Supervisors: Prof. J.J. Lukkien Eindhoven
More informationThe Next Generation of Compression JPEG 2000
The Next Generation of Compression JPEG 2000 Bernie Brower NSES Kodak bernard.brower@kodak.com +1 585 253 5293 1 What makes JPEG 2000 Special With advances in compression science combined with advances
More informationOverview: motioncompensated coding
Overview: motioncompensated coding Motioncompensated prediction Motioncompensated hybrid coding Motion estimation by blockmatching Motion estimation with subpixel accuracy Power spectral density of
More informationPartial Video Encryption Using Random Permutation Based on Modification on Dct Based Transformation
International Refereed Journal of Engineering and Science (IRJES) ISSN (Online) 2319183X, (Print) 23191821 Volume 2, Issue 6 (June 2013), PP. 5458 Partial Video Encryption Using Random Permutation Based
More informationCMPT 365 Multimedia Systems. Media Compression  Video
CMPT 365 Multimedia Systems Media Compression  Video Spring 2017 Edited from slides by Dr. Jiangchuan Liu CMPT365 Multimedia Systems 1 Introduction What s video? a timeordered sequence of frames, i.e.,
More informationECE 499/599 Data Compression & Information Theory. Thinh Nguyen Oregon State University
ECE 499/599 Data Compression & Information Theory Thinh Nguyen Oregon State University Adminstrivia Office Hours TTh: 23 PM Kelley Engineering Center 3115 Class homepage http://www.eecs.orst.edu/~thinhq/teaching/ece499/spring06/spring06.html
More informationSPIHTBASED IMAGE ARCHIVING UNDER BIT BUDGET CONSTRAINTS
SPIHTBASED IMAGE ARCHIVING UNDER BIT BUDGET CONSTRAINTS by Yifeng He A thesis submitted in conformity with the requirements for the degree of Master of Applied Science, Graduate School of Electrical Engineering
More informationImage Processing. Blending. Blending in OpenGL. Image Compositing. Blending Errors. Antialiasing Revisited Computer Graphics I Lecture 15
15462 Computer Graphics I Lecture 15 Image Processing Blending Display Color Models Filters Dithering Image Compression March 18, 23 Frank Pfenning Carnegie Mellon University http://www.cs.cmu.edu/~fp/courses/graphics/
More informationVery Low Bit Rate Color Video
1 Very Low Bit Rate Color Video Coding Using Adaptive Subband Vector Quantization with Dynamic Bit Allocation Stathis P. Voukelatos and John J. Soraghan This work was supported by the GECMarconi Hirst
More informationISSN Vol.03,Issue.09 May2014, Pages:
www.semargroup.org, www.ijsetr.com ISSN 23198885 Vol.03,Issue.09 May2014, Pages:17801785 JPEG Image Compression and Decompression using Discrete Cosine Transform (DCT) EI EI PO 1, NANG AE AE TE 2 1
More informationPriyanka Dixit CSE Department, TRUBA Institute of Engineering & Information Technology, Bhopal, India
An Efficient DCT Compression Technique using Strassen s Matrix Multiplication Algorithm Manish Manoria Professor & Director in CSE Department, TRUBA Institute of Engineering &Information Technology, Bhopal,
More informationIMAGE PROCESSING USING DISCRETE WAVELET TRANSFORM
IMAGE PROCESSING USING DISCRETE WAVELET TRANSFORM Prabhjot kour Pursuing M.Tech in vlsi design from Audisankara College of Engineering ABSTRACT The quality and the size of image data is constantly increasing.
More informationJPEG IMAGE CODING WITH ADAPTIVE QUANTIZATION
JPEG IMAGE CODING WITH ADAPTIVE QUANTIZATION Julio Pons 1, Miguel Mateo 1, Josep Prades 2, Román Garcia 1 Universidad Politécnica de Valencia Spain 1 {jpons,mimateo,roman}@disca.upv.es 2 jprades@dcom.upv.es
More informationChapter 3: Multimedia Systems  Communication Aspects and Services Chapter 4: Multimedia Systems Storage Aspects Chapter 5: Multimedia Usage
Chapter : Basics Audio Technology Images and Graphics Video and Animation.: Images and Graphics Digital image representation Image formats and color models JPEG, JPEG Image synthesis and graphics systems
More informationAdjustable Compression Method for Still JPEG Images
Adjustable Compression Method for Still JPEG Images Jerónimo Mora Pascual, Higinio Mora Mora, Andrés Fuster Guilló, Jorge Azorín López Specialized Processors Architecture Laboratory Department of Computer
More information10.2 Video Compression with Motion Compensation 10.4 H H.263
Chapter 10 Basic Video Compression Techniques 10.11 Introduction to Video Compression 10.2 Video Compression with Motion Compensation 10.3 Search for Motion Vectors 10.4 H.261 10.5 H.263 10.6 Further Exploration
More informationAN OPTIMIZED LOSSLESS IMAGE COMPRESSION TECHNIQUE IN IMAGE PROCESSING
AN OPTIMIZED LOSSLESS IMAGE COMPRESSION TECHNIQUE IN IMAGE PROCESSING 1 MAHENDRA PRATAP PANIGRAHY, 2 NEERAJ KUMAR Associate Professor, Department of ECE, Institute of Technology Roorkee, Roorkee Associate
More informationWavelet Based Image Compression Using ROI SPIHT Coding
International Journal of Information & Computation Technology. ISSN 09742255 Volume 1, Number 2 (2011), pp. 6976 International Research Publications House http://www.irphouse.com Wavelet Based Image
More informationPerformance Comparison between DWTbased and DCTbased Encoders
, pp.8387 http://dx.doi.org/10.14257/astl.2014.75.19 Performance Comparison between DWTbased and DCTbased Encoders Xin Lu 1 and Xuesong Jin 2 * 1 School of Electronics and Information Engineering, Harbin
More informationEFFICIENT DEISGN OF LOW AREA BASED H.264 COMPRESSOR AND DECOMPRESSOR WITH H.264 INTEGER TRANSFORM
EFFICIENT DEISGN OF LOW AREA BASED H.264 COMPRESSOR AND DECOMPRESSOR WITH H.264 INTEGER TRANSFORM 1 KALIKI SRI HARSHA REDDY, 2 R.SARAVANAN 1 M.Tech VLSI Design, SASTRA University, Thanjavur, Tamilnadu,
More informationBlockMatching based image compression
IEEE Ninth International Conference on Computer and Information Technology BlockMatching based image compression YunXia Liu, Yang Yang School of Information Science and Engineering, Shandong University,
More informationComparison of Digital Image Watermarking Algorithms. Xu Zhou Colorado School of Mines December 1, 2014
Comparison of Digital Image Watermarking Algorithms Xu Zhou Colorado School of Mines December 1, 2014 Outlier Introduction Background on digital image watermarking Comparison of several algorithms Experimental
More informationDigital Image Processing
Digital Image Processing Third Edition Rafael C. Gonzalez University of Tennessee Richard E. Woods MedData Interactive PEARSON Prentice Hall Pearson Education International Contents Preface xv Acknowledgments
More informationJPEG Joint Photographic Experts Group ISO/IEC JTC1/SC29/WG1 Still image compression standard Features
JPEG2000 Joint Photographic Experts Group ISO/IEC JTC1/SC29/WG1 Still image compression standard Features Improved compression efficiency (vs. JPEG) Highly scalable embedded data streams Progressive lossy
More informationA QUADTREE DECOMPOSITION APPROACH TO CARTOON IMAGE COMPRESSION. YiChen Tsai, MingSui Lee, Meiyin Shen and C.C. Jay Kuo
A QUADTREE DECOMPOSITION APPROACH TO CARTOON IMAGE COMPRESSION YiChen Tsai, MingSui Lee, Meiyin Shen and C.C. Jay Kuo Integrated Media Systems Center and Department of Electrical Engineering University
More informationModule 7 VIDEO CODING AND MOTION ESTIMATION
Module 7 VIDEO CODING AND MOTION ESTIMATION Lesson 20 Basic Building Blocks & Temporal Redundancy Instructional Objectives At the end of this lesson, the students should be able to: 1. Name at least five
More informationCOMPARISONS OF DCTBASED AND DWTBASED WATERMARKING TECHNIQUES
COMPARISONS OF DCTBASED AND DWTBASED WATERMARKING TECHNIQUES H. I. Saleh 1, M. E. Elhadedy 2, M. A. Ashour 1, M. A. Aboelsaud 3 1 Radiation Engineering Dept., NCRRT, AEA, Egypt. 2 Reactor Dept., NRC,
More informationDIGITAL IMAGE COMPRESSION TECHNIQUES
DIGITAL IMAGE COMPRESSION TECHNIQUES Gomathi.K.V 1, Lotus.R 2 1 Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India 2 Centre for Information
More informationMpeg 1 layer 3 (mp3) general overview
Mpeg 1 layer 3 (mp3) general overview 1 Digital Audio! CD Audio:! 16 bit encoding! 2 Channels (Stereo)! 44.1 khz sampling rate 2 * 44.1 khz * 16 bits = 1.41 Mb/s + Overhead (synchronization, error correction,
More informationHybrid ART/Kohonen Neural Model for Document Image Compression. Computer Science Department NMT Socorro, New Mexico {hss,
Hybrid ART/Kohonen Neural Model for Document Image Compression Hamdy S. Soliman Mohammed Omari Computer Science Department NMT Socorro, New Mexico 87801 {hss, omari01}@nmt.edu Keyword 1: Learning Algorithms
More informationChapter 7 Multimedia Operating Systems
MODERN OPERATING SYSTEMS Third Edition ANDREW S. TANENBAUM Chapter 7 Multimedia Operating Systems Introduction To Multimedia (1) Figure 71. Video on demand using different local distribution technologies.
More informationPipelined Fast 2D DCT Architecture for JPEG Image Compression
Pipelined Fast 2D DCT Architecture for JPEG Image Compression Luciano Volcan Agostini agostini@inf.ufrgs.br Ivan Saraiva Silva* ivan@dimap.ufrn.br *Federal University of Rio Grande do Norte DIMAp  Natal
More informationJPEG compression of monochrome 2Dbarcode images using DCT coefficient distributions
Edith Cowan University Research Online ECU Publications Pre. JPEG compression of monochrome Dbarcode images using DCT coefficient distributions Keng Teong Tan Hong Kong Baptist University Douglas Chai
More informationScalable Compression and Transmission of Large, Three Dimensional Materials Microstructures
Scalable Compression and Transmission of Large, Three Dimensional Materials Microstructures William A. Pearlman Center for Image Processing Research Rensselaer Polytechnic Institute pearlw@ecse.rpi.edu
More informationLecture 4: Video Compression Standards (Part1) Tutorial 2 : Image/video Coding Techniques. Basic Transform coding Tutorial 2
Lecture 4: Video Compression Standards (Part1) Tutorial 2 : Image/video Coding Techniques Dr. Jian Zhang Conjoint Associate Professor NICTA & CSE UNSW COMP9519 Multimedia Systems S2 2006 jzhang@cse.unsw.edu.au
More informationA MiniatureBased Image Retrieval System
A MiniatureBased Image Retrieval System Md. Saiful Islam 1 and Md. Haider Ali 2 Institute of Information Technology 1, Dept. of Computer Science and Engineering 2, University of Dhaka 1, 2, Dhaka1000,
More information2.4 Audio Compression
2.4 Audio Compression 2.4.1 Pulse Code Modulation Audio signals are analog waves. The acoustic perception is determined by the frequency (pitch) and the amplitude (loudness). For storage, processing and
More informationWavelet Transform (WT) & JPEG2000
Chapter 8 Wavelet Transform (WT) & JPEG2000 8.1 A Review of WT 8.1.1 Wave vs. Wavelet [castleman] 1 01 23 45 67 8 0 100 200 300 400 500 600 Figure 8.1 Sinusoidal waves (top two) and wavelets (bottom
More informationMPEG2. ISO/IEC (or ITUT H.262)
MPEG2 1 MPEG2 ISO/IEC 138182 (or ITUT H.262) High quality encoding of interlaced video at 415 Mbps for digital video broadcast TV and digital storage media Applications Broadcast TV, Satellite TV,
More informationFPGA based High Performance CAVLC Implementation for H.264 Video Coding
FPGA based High Performance CAVLC Implementation for H.264 Video Coding Arun Kumar Pradhan Trident Academy of Technology Bhubaneswar,India Lalit Kumar Kanoje Trident Academy of Technology Bhubaneswar,India
More informationContext based optimal shape coding
IEEE Signal Processing Society 1999 Workshop on Multimedia Signal Processing September 1315, 1999, Copenhagen, Denmark Electronic Proceedings 1999 IEEE Context based optimal shape coding Gerry Melnikov,
More informationData Compression. Media Signal Processing, Presentation 2. Presented By: Jahanzeb Farooq Michael Osadebey
Data Compression Media Signal Processing, Presentation 2 Presented By: Jahanzeb Farooq Michael Osadebey What is Data Compression? Definition Reducing the amount of data required to represent a source
More informationScalable Perceptual and Lossless Audio Coding based on MPEG4 AAC
Scalable Perceptual and Lossless Audio Coding based on MPEG4 AAC Ralf Geiger 1, Gerald Schuller 1, Jürgen Herre 2, Ralph Sperschneider 2, Thomas Sporer 1 1 Fraunhofer IIS AEMT, Ilmenau, Germany 2 Fraunhofer
More informationPROBABILISTIC MEASURE OF COLOUR IMAGE PROCESSING FIDELITY
Journal of ELECTRICAL ENGINEERING, VOL. 59, NO. 1, 8, 9 33 PROBABILISTIC MEASURE OF COLOUR IMAGE PROCESSING FIDELITY Eugeniusz Kornatowski Krzysztof Okarma In the paper a probabilistic approach to quality
More informationInternational Journal of Advancements in Research & Technology, Volume 2, Issue 8, August ISSN
International Journal of Advancements in Research & Technology, Volume 2, Issue 8, August2013 244 Image Compression using Singular Value Decomposition Miss Samruddhi Kahu Ms. Reena Rahate Associate Engineer
More informationOvercompressing JPEG images with Evolution Algorithms
Author manuscript, published in "EvoIASP2007, Valencia : Spain (2007)" Overcompressing JPEG images with Evolution Algorithms Jacques Lévy Véhel 1, Franklin Mendivil 2 and Evelyne Lutton 1 1 Inria, Complex
More informationLecture 5: Video Compression Standards (Part2) Tutorial 3 : Introduction to Histogram
Lecture 5: Video Compression Standards (Part) Tutorial 3 : Dr. Jian Zhang Conjoint Associate Professor NICTA & CSE UNSW COMP9519 Multimedia Systems S 006 jzhang@cse.unsw.edu.au Introduction to Histogram
More informationISSN V. Bhagya Raju 1, Dr K. Jaya Sankar 2, Dr C.D. Naidu 3
Performance Evaluation of Basic Compression Technique for Wireless Text Data ISSN 22783091 V. Bhagya Raju 1, Dr K. Jaya Sankar 2, Dr C.D. Naidu 3 1 Prof & HOD, ECE Dept Vidya Vihar Institute of Technology
More informationF..\ Compression of IP Images for Autostereoscopic 3D Imaging Applications
Compression of IP Images for Autostereoscopic 3D Imaging Applications N.P.Sgouros', A.G.Andreou', MSSangriotis', P.G.Papageorgas2, D.M.Maroulis', N.G.Theofanous' 1. Dep. of Informatics and Telecommunications,
More informationLowComplexity, NearLossless Coding of Depth Maps from KinectLike Depth Cameras
LowComplexity, NearLossless Coding of Depth Maps from KinectLike Depth Cameras Sanjeev Mehrotra, Zhengyou Zhang, Qin Cai, Cha Zhang, Philip A. Chou Microsoft Research Redmond, WA, USA {sanjeevm,zhang,qincai,chazhang,pachou}@microsoft.com
More informationA Comparative Study between Two Hybrid Medical Image Compression Methods
A Comparative Study between Two Hybrid Medical Image Compression Methods Clarissa Philana Shopia Azaria 1, and Irwan Prasetya Gunawan 2 Abstract This paper aims to compare two hybrid medical image compression
More informationHuffman Coding Author: Latha Pillai
Application Note: Virtex Series XAPP616 (v1.0) April 22, 2003 R Huffman Coding Author: Latha Pillai Summary Huffman coding is used to code values statistically according to their probability of occurence.
More informationBiometrics Technology: Image Processing & Pattern Recognition (by Dr. Dickson Tong)
Biometrics Technology: Image Processing & Pattern Recognition (by Dr. Dickson Tong) References: [1] http://homepages.inf.ed.ac.uk/rbf/hipr2/index.htm [2] http://www.cs.wisc.edu/~dyer/cs540/notes/vision.html
More informationLecture 6 Review of Lossless Coding (II)
Shujun LI (李树钧): INF1084520091 Multimedia Coding Lecture 6 Review of Lossless Coding (II) May 28, 2009 Outline Review Manual exercises on arithmetic coding and LZW dictionary coding 1 Review Lossy coding
More informationEntropy Driven Bit Coding For Image Compression In Medical Application
IOSR Journal of Computer Engineering (IOSRJCE) eissn: 22780661,pISSN: 22788727, Volume 19, Issue 3, Ver. III (May  June 2017), PP 5360 www.iosrjournals.org Entropy Driven Bit Coding For Image Compression
More informationOptimum Global Thresholding Based Variable Block Size DCT Coding For Efficient Image Compression
Biomedical & Pharmacology Journal Vol. 8(1), 453461 (2015) Optimum Global Thresholding Based Variable Block Size DCT Coding For Efficient Image Compression VIKRANT SINGH THAKUR 1, SHUBHRATA GUPTA 2 and
More informationImplication of variable code block size in JPEG 2000 and its VLSI implementation
Implication of variable code block size in JPEG 2000 and its VLSI implementation PingSing Tsai a, Tinku Acharya b,c a Dept. of Computer Science, Univ. of Texas Pan American, 1201 W. Univ. Dr., Edinburg,
More informationChapter 7 Lossless Compression Algorithms
Chapter 7 Lossless Compression Algorithms 7.1 Introduction 7.2 Basics of Information Theory 7.3 RunLength Coding 7.4 VariableLength Coding (VLC) 7.5 Dictionarybased Coding 7.6 Arithmetic Coding 7.7
More informationChapter 3 Set Redundancy in Magnetic Resonance Brain Images
16 Chapter 3 Set Redundancy in Magnetic Resonance Brain Images 3.1 MRI (magnetic resonance imaging) MRI is a technique of measuring physical structure within the human anatomy. Our proposed research focuses
More information