JJM-007-208 A COMPARATIVE STUDY ON DIGITAL IMAGE COMPRESSION AND DECOMPRESSION Rekha R Pillai 1, Dr.K.Kanagalekshmi 2 1 PhD Research Scholar, Dept. of Computer Science, Nehru Arts and Science College, Coimbatore, Tamilnadu - 641105, India E-mail:rekharpillai2017@gmail.com 2 Assistant Professor, Dept. of Computer Science, Nehru Arts and Science College, Coimbatore, Tamilnadu - 641105, India E-mail:kkanagalakshmi@gmail.com ABSTRACT: Digital Image Compression is an emerging technology in the IT world. In the digital world most of the data of the public is related to image. Especially, Digital documents, Biometric features and other records are based on the images. An image consumes more memory than the text files. To avoid more memory consumption by the image, the size of the image can be reduced using compression techniques. This compression techniques reducing the size of the image without affecting the original image by using some algorithms. There are different types of compression techniques are available. They are classified into two such as Lossy and Lossless image compression techniques. This paper is aimed to study about compression techniques and to analyses the pros and cons of each method theoretically. Keywords: Image Compression, Image Decompression, Lossy and Lossless Compression Techniques, Redundancy INTRODUCTION The Main intention of the image compression is to reduce the redundancy of an image then store and transmit image data through network in efficient form. Various types of compression methods are used in Digital Image Processing to compress the images. The compression techniques are classified into two like Lossy and Lossless Compression. Lossy Compression will accept high compression ratio for example 50:1 or higher. Lossy Compression allows same degradation process also. But still it cannot completely recover the original data. It s one of the drawbacks of Lossy compression. Next is Lossless compression, this is used to overcome the drawback of Lossy compression. 87
Lossless compression recovers the original image or data. But it can reduce the size to around 2:1. In Lossless compression, the original image is exactly reconstructed after the decompression process. It is mainly used for artificial images which are icons, clip arts, comics or technical drawings. In Lossy, original image is not confirmed after the decompression and accuracy. But reconstruction is traded with efficiency of compression. It is mainly used to compress multimedia applications such as image, audio and video which are used for activity of (i) internet and media. (ii) compress multimedia applications such as image, audio and video which are used for activity of internet and media. The most important work is reducing redundancy and irrelevancy. [1]. The fundamental principles used in image compression for avoid redundancy and irrelevant information. Compression is achieved by removal of one or more of the three basic data redundancies. In Sender side perform the steps such as (i) Read the original message (ii) Perform compression using any of suitable techniques. (iii) Transform the compressed data to the destination. In Receiver side also perform Read the compressed data. Decompress using any of standard technique. 1. Coding redundancy. 2. Inter pixel redundancy. 3. Psycho-visual redundancy. Coding redundancy:-which is present when less than the smallest length code words are used. Inter pixel redundancy:-which is due to correlation between pixels. Psycho visual redundancy: - which is results from data that is ignored by human visual system [15]. The given Fig-1 shows, Fig-1: Compression and Decompression process diagram This paper consist of six sections. Section-2 describe fundamentals of image compression and decompression, Section-3 furnishes the advantage and disadvantage of compression, Section-4 explains about Lossy and Lossless 88
compression technique, Section-5 list out benefits of image compression and decompression, Section-6 conclude the study IMAGE COMPRESSION AND DECOMPRESSION Image compression is an application of data compression that encodes the original image with few bits. The objective of image compression is to reduce the redundancy of the image and to store or transmit data in an efficient form. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. It involves minimization of the number of information carrying units, pixels. This means that an image where adjacent pixels have almost the same values leads to spatial redundancy. 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.image compression is the application of data compression on digital images. Image compression is used to minimize the amount of memory needed to represent an image [3] [4]. Images often require a large number of bits to represent them, and if the image needs to be transmitted or stored, it is impractical to do so without somehow reducing the number of bits [3]. The Main intention of the image compression is to reduce the redundancy of an image store and transmit image data through network in efficient form. Various types of compression methods are used in Digital Image processing for compress the images. These compression algorithm classified into two Lossy and Lossless Compression. Lossless compression is preferred for archival purposes and often for medical imaging, technical drawings, clip art, or comics. Lossy compression methods, especially when used at low rates, introduce compression artifacts. Lossy methods are especially suitable for natural images such as photographs in applications where minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a substantial reduction in bit rate. The lossy compression that produces imperceptible differences may be called visually lossless [5]. Fig-2: Block diagram of image compression system The main aim of image compression is- 89
Digital images require large amounts of space for storage and large bandwidths for transmission. A 640 x 480 color image requires close to 1MB of space. The goal of image compression is to reduce the amount of data required to represent a digital image. Reduce storage requirements and increase transmission rates. Reduction of the number of bits needed to represent a given image or its information. Image compression exploits the fact that all images are not equally likely [6] The major goal of the image decompression is to image compression is to decode and reconstruct the original image. It is an application to get a much better image in terms of quality or size and also we can get original images from their compressed form where we need that the quality of the image is high whether it may be have higher. COMPRESSION METHODS ADVANTAGE AND DISADVANTAGES There are six different types of compression techniques are available. Table below shows the comparison between Compression Techniques Method Advantages Disadvantages Image compression by wavelet transform VQ Compression Via Finding Orthogonal Transform Compression Of Data Encrypted With Block Ciphers Compression Of Encrypted Images High compression ratio Simple decoder No-coefficient quantization Better Compression Efficiency Better Compression Efficiency Simple Coefficient quantization, Bit allocation Slow codebook generation Complex Method Fundamental Compression Less Compression Gain is available Compression via Arithmetic Coding Leads to preservation of the probability mass function (PMF) [7] of prediction error Table -1: Comparison between Compression Techniques Limit to the precision of the number which can be encoded COMPARISON BETWEEN LOSSLESS AND LOSSY TECHNIQUES The image compression techniques are broadly classified into two categories depending 90
whether or not an exact model of the original image could be reconstructed using the compressed image. These are: 1. Lossless technique 2. Lossy technique In lossless compression, the reconstructed image after compression is numerically identical to the original image [8]. In lossy compression scheme, the reconstructed image contains degradation relative to the original. Lossy technique causes image quality degradation in each compression or decompression step. In general, lossy techniques provide for greater compression ratios than lossless techniques. The following are the some of the lossless and lossy data compression techniques: A. Lossless coding techniques a. Run length encoding b. Huffman encoding c. Arithmetic encoding d. Entropy coding e. Area coding B.Lossy coding techniques a. Predictive coding b. Transform coding (FT/DCT/Wavelets) [9] A) Lossless Compression Techniques (Reversible Compression) Lossless compression compresses the image by encoding all the information from the original file, so when the image is decompressed, it will be exactly identical to the original image. Examples of lossless [10] image compression are PNG and GIF. When to use a certain image compression format really depends on what is being compressed. Fig-3: Reversible compression 91
Lossy image compression is a three step algorithm (Fig 3): In the first step the original image is transformed in order to eliminate the inter-pixel redundancy. Then, quantization is done to remove psycho-visual redundancy. The bits are then encoded to get more compression from the coding redundancy [2]. 1) Run Length Encoding: Run-length encoding (RLE) is a very simple form of image compression in which runs of data are stored as a single data value and count, rather than as the original run. It is used for sequential data and it is helpful for repetitive data. In this technique replaces sequences of identical symbol (pixel), called runs. This is most useful on data that contains many such runs for example, simple graphic images such as icons, line drawings, and animations. It is not useful with files that don't have many runs as it could greatly increase the file size. Runlength encoding performs lossless image compression [11]. Run-length encoding is used in fax machines. 2) Entropy Encoding: In information theory an entropy encoding is a lossless data compression scheme that is independent of the specific characteristics of the medium. One of the main types of entropy coding creates and assigns a unique prefix-free code for each unique symbol that occurs in the input. These entropy encoders then compress the image by replacing each fixed-length input symbol with the corresponding variable-length prefix free output codeword. 3) Huffman Encoding: In computer science and information theory, Huffman coding is an entropy encoding algorithm used for lossless data compression. It was developed by Huffman. Huffman coding [12] today is often used as a "back-end" to some other compression methods. The term refers to the use of a variable-length code table for encoding a source symbol where the variablelength code table has been derived in a particular way based on the estimated probability of occurrence for each possible value of the source symbol. The pixels in the image are treated as symbols. The symbols which occur more frequently are assigned a smaller number of bits, while the symbols that occur less frequently are assigned a relatively larger number of bits. Huffman code is a prefix code. This means that the (binary) code of any symbol is not the prefix of the code of any other symbol. 4) Arithmetic Coding: Arithmetic coding is a form of entropy encoding used in lossless data compression. Normally, a string of characters such as the words "hello there" is represented using a fixed number of bits per character, as in the ASCII code. When a string is converted to arithmetic encoding, frequently used characters will be stored with little bits and not-so-frequently occurring characters will be stored with more bits, resulting in fewer bits used in total. Arithmetic coding differs from other forms of entropy encoding such as Huffman coding[9] 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. 92
B) Lossy Compression Techniques (Irreversible Compression) Lossy compression as the name implies leads to loss of some information. The compressed image is similar to the original uncompressed image but not just like the previous as in the process of compression [13] some information concerning the image has been lost. They are typically suited to images. The most common example of lossy compression is JPEG. An algorithm that restores the presentation to be the same as the original image is known as lossy techniques. Reconstruction of the image is an approximation of the original image, therefore the need of measuring of the quality of the image for lossy compression technique. Lossy compression technique provides a higher compression ratio than lossless compression. Major performance considerations of a lossy compression scheme include: 1) Compression ratio 2) Signal to noise ratio 3) Speed of encoding & decoding Fig-4: Irreversible compression Lossless image compression is a two-step algorithm: In the first step the original image is transformed in order to eliminate the interpixel redundancy. In the second step, an entropy coder is used to remove coding redundancy[2]. Lossy image compression techniques include following schemes: 1) Scalar Quantization: The most common type of quantization is known as scalar quantization. Scalar quantization, typically denoted as Y=Q (x), is the process of using a quantization function Q to map a scalar (onedimensional) input value x to a scalar output value Y. Scalar quantization can be as simple and intuitive as rounding high-precision numbers to the nearest integer, or to the nearest multiple of some other unit of precision. 2) Vector Quantization: Vector quantization (VQ) is a classical quantization technique from signal processing which allows the modelling of probability density functions by the distribution of prototype vectors. It was originally used for image compression. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. The 93
density matching property of vector quantization is powerful, especially for identifying the density of large and highdimensioned data. Since data points are represented by the index of their closest centriod commonly occurring data have low error, and rare data high error. This is why VQ is suitable for lossy data compression. It can also be used for lossy data correction and density estimation [14] BENEFITS OF COMPRESSION It provides a potential cost savings associated with sending less data over switched telephone network where cost of call is really usually based upon its duration. It not only reduces storage requirements but also overall execution time. It also reduces the probability of transmission errors since fewer bits are transferred. It also provides a level of security against illicit monitoring [14]. ADVANTAGE AND DISADVANTAGE OF IMAGE COMPRESSION Advantage Disadvantage Less disk space Added complication The quality of bit used to Effect of errors in transmission store the data is reduced Faster insertion and deletion Slower for sophiscated methods Byte order independent Unknown byte\pixel relationship Faster file transfer Need to decompress all previous data ADVANTAGE AND DISADVANTAGE OF IMAGE DECOMPRESSION Advantage Quality of the image is maintained Disadvantage Require more disk space More advantageous where The quality of bit used to store the HD pictures are required data is increased Need not to decompress Slower insertion and deletion further Fine byte/pixel relationship Slower file transfer 94
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