Digital Image and Video Compression Techniques. Bhupinderjit kaur. Abstract –
Internet is very essential for any form of communication. The average net user ...
International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 7, July 2013)
Digital Image and Video Compression Techniques Bhupinderjit kaur Abstract – Internet is very essential for any form of communication. The average net user and communicated through text, images and videos, of these the simplest and easiest is through text as it is quick as simple to use as sending text very little bandwidth. The problem with images and videos is that they require large bandwidth the send and receive. Therefore, there is a need to decrease the size of the image or video sent or received. So, we use technique called data compression techniques. The main aim of data compression is to reduce the amount of data needed to accurately represent an image, such that this image can be accurately transmitted or archived.
These are called noiseless since they do not add noise to the signal. It is also known as entropy coding since it use statistics/decompression techniques to eliminate/minimize redundancy. Lossless compression is used for only few applications with stringent requirements such as medical imaging. Following techniques are included in lossless compression 1. Run length Coding 2. Huffman Coding 3. LZW( Lempel Ziv- Welch) Coding 4. Area Coding
Keywords– Image Compression, Image Compression Techniques, Lossless Compression Techniques and Lossy Compression Techniques.
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B. Lossy Compression Technique In Lossy compression technique, the image that is regenerated by the decompression process is not the original copy of the image that is compresed. Some data might be lost during the technique. These compression technique are cheaper that is they take less time and space when it comes to sending millions of bits per second for images and video. Several methods have been developed using lossy compression techniques. JPEG( Joint Photographic Experts Group) encoding is used to compress pictures and graphics, MPEG( Moving Picture Experts Group) encoding is used to compress video, and MP3 ( MPEG audio layer 3) for audio compression.
INTRODUCTION
Image compression addresses the problem of reducing the amount of data required to represent a digital image. It is a process intended to yield a compact representation of an image, thereby reducing the image storage/transmission requirements. Compression is achieved by the removal of one or more of the three basic data redundancies: 1. Coding Redundancy 2. Interpixel Redundancy 3. Psychovisual Redundancy Coding redundancy is present when less than optimal code words are used. Interpixel redundancy results from correlations between the pixels of an image. Psychovisual redundancy is due to data that is ignored by the human visual system ( irrelevant information). II.
IMAGE COMPRESSION T ECHNIQUES
The image compression techniques are broadly classified into two categories depending whether or not an exact replica of original image could be reconstructed using the compressed image. These are: 1. Lossless Technique 2. Lossy Technique
Figure 1: Outline of lossy image compression
As shown above the outline of lossy compression techniques. In this prediction – transformation – decomposition process is completely reversible. The quantization process results in loss of information. The entropy coding after the quantization step, however , is lossless. The decoding is reverse process.
A. Lossless Compression Technique In Lossless Compression Technique, the image or video that is compressed that can be regenerated by using the decompression technique: no part of data is lost in the process.
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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 7, July 2013) Firstly, entropy decoding is applied to compressed data to get the quantized data. Secondly, dequantization is applied to it & finally the inverse transformation to get the reconstructed image. Major performance considerations of a lossy compression scheme include: 1. Compression ratio 2. Signal – to – noise ratio 3. Speed of encoding & decoding Lossy compression techniques includes following schemes: 1. Transformation Coding 2. Vector Quantization 3. Fractal Coding 4. Block Truncation Coding 5. Sub band Coding
2) Huffman coding : This is a general technique for coding symbols based on their statistical occurrence frequencies (probabilities). The pixels in the image are treated as symbols. The symbols that 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. Most image coding standards use lossy techniques in the prefix of the code of any other symbol. Most image coding standards use lossy techniques in the earlier stages of compression and use Huffman coding as the final step. 3) LZW coding : LZW (Lempel- Ziv – Welch ) is a dictionary based coding. The idea is to create a dictionary (a table) of strings used during the communication session. If both the sender and the receiver have a copy of the dictionary, then previously-encountered strings can be substituted by their index in the dictionary to reduce the amount of information transmitted. Dictionary based coding can be static or dynamic. In static dictionary coding, dictionary is fixed during the encoding and decoding processes. In dynamic dictionary coding, the dictionary is updated on fly. LZW is widely used in computer industry and is implemented as compress command on UNIX.
C. Lossless Compression Techniques 1) Run Length Coding: Run length coding is the simplest method of compression. It can be used to compress data made of any combination of symbols. It does not need to know the frequency of occurrence of symbols and can be very efficient if data is represented as 0s and 1s. The general idea behind this method is to replace consecutive repeating occurrences of a symbol by one occurrence of the symbol followed by the number of occurrences. The method can be even more efficient if the data uses only two symbols (for example 0 and 1) in its bit pattern and one symbol is more frequent than the other.
Compression : In this phase there are two concurrent events: building an indexed dictionary and compressing a string of symbols. The algorithm extracts the smallest substring that cannot be found in the dictionary from the remaining uncompressed string. It then stores a copy of this substring in the dictionary as a new entry and assign it an index value. Compression occurs when the substring , expect for the last character , is replaced with the index found in the dictionary. The process then inserts the index and the last character, is replaced with the index found in the dictionary. The process then inserts the index and the last character of the substring into the compressed string.
Figure 2: Run Length coding
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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 7, July 2013)
Figure 4: An example of Lempel Ziv Decoding
4) Area Coding : Area coding is an enhanced form of run length coding, reflecting the two dimenstional characters of images. This is a significant advance over the lossless methods. For coding an image it does not make too much sense to interpret it as a sequential stream, as it is in fact an array of sequences, building up a two dimenstional object. Therefore as a the two dimensions are independent and of same importance, it is obvious that a coding scheme aware of this has some advantages. The algorithms for area coding try to find rectangular regions with the same characteristics. These regions are in a coded in descriptive form as an element with two points and a certain structure. This type of coding is highly effective but it bears the problem of a nonlinear method, which cannot be implemented in hardware. Therefore, the performance in terms of compression time is not competitive, although the compression ratio is.
Figure 3: An example of Lempel Ziv encoding
Decompression : Decompression is the inverse of compression process. The process extracts the substrings from the compressed string and tries to replace the indexes with the corresponding entry in the dictionary, which is empty at first and built up gradually. The idea is that when the index is received, there is already an entry in the dictionary corresponding to that index.
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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 7, July 2013) D. Lossy Compression Techniques 1) Transformation Coding : A general transform coding scheme involves subdividing an N x N image into smaller n x n blocks and performing a unitary transform on each sub image. A unitary transform is a reversible linear transform whose kernel describes a set of complete, orthonormal discrete basic functions. The goal of the transform is to decorrelate the original signal, and this decorrelation generally results in the signal energy being redistributed among only a small set of transform coefficients. In this way, many coefficients may be discarded after quantization and prior to encoding. Also, visually lossless compression can often be achieved by incorporating the HVS contrast sensitivity function in the quantization of the coefficients. Transform coding can be generalized into four stages:
3) Fractal Coding : The essential idea here is to decompose the image into seg-ments by using standard image processing techniques such as color separation, edge detection, and spectrum and texture analysis. Then each segment is looked up in a library of fractals. The library actually contains codes called iterated function system (IFS) codes, which are compact sets of numbers. Using a systematic procedure, a set of codes for a given image are determined, such that when the IFS codes are applied to a suitable set of image blocks yield an image that is a very close approximation of the original. This scheme is highly effective for compressing images that have good regularity and self-similarity. 4) Block Truncation Coding : In this scheme, the image is divided into non overlapping blocks of pixels. For each block, threshold and reconstruction values are determined. The threshold is usually the mean of the pixel values in the block. Then a bitmap of the block is derived by replacing all pixels whose values are greater than or equal (less than) to the threshold by a 1 (0). Then for each segment (group of 1s and 0s) in the bitmap, the reconstruction value is determined. This is the average of the values of the corresponding pixels in the original block. 5) Sub band coding : In this scheme, the image is analyzed to produce the components containing frequencies in welldefined bands, the sub bands. Subsequently, quantization and coding is applied to each of the bands. The advantage of this scheme is that the quantization and coding well suited for each of the sub bands can be designed separately.
image subdivision image transformation coefficient quantization Huffman coding For a transform coding scheme, logical model ling is done into two steps: a segmentation one, in which image is subdivided in bidimenstional vectors (possibly of different sizes) and a transformation step, in which the chosen transform coding( e.g KLT,DCT,Hadamard ) is applied. Quantization can be performed in several ways. Most classical approaches use „zonal coding‟ consisting in the scalar quantization of the coefficients belonging to a predefined area( with a fixed bit allocation), and a „threshold coding‟ consisting in the choice of the coefficients of each block characterized by an absolute exceeding a predefined threshold. The same type of encoding is used for each coding method. In most cases a classical Huffman coding can be used successfully. The JPEG and MPEG standards are examples of standards based on transform coding. 2) Vector Quantization : The basic idea in this technique is to develop a dictionary of fixed-size vectors, called code vectors. A vector is usually a block of pixel values. A given image is then partitioned into non-overlapping blocks (vectors) called image vectors. Then for each in the dictionary is determined and its index in the dictionary is used as the encoding of the original image vector. Thus, each image is represented by a sequence of indices that can be further entropy coded.
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CONCLUSION
This paper presents various types of image compression and image compression techniques. There are basically two types of compression techniques. One is Lossless Compression and other is Lossy Compression Technique. In Lossless Compression Technique, the image or video that is compressed that can be regenerated by using the decompression technique: no part of data is lost in the process. In Lossy compression technique, the image that is regenerated by the decompression process is not the original copy of the image that is compresed. Some data might be lost during the technique.
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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 7, July 2013) [4 ] Ismail Avcibas, Nasir Memon, Bulent Sankur, Khalid Sayood, “ A Progressive Lossless / Near Lossless Image Compression Algorithm,”IEEE Signal Processing Letters, vol. 9, No. 10, pp 312314, October 2002. [5 ] Rafeal C. Gonzalez, Richard E. Woods DIGITAL IMAGE PROCESSING 3rd edition. [6 ] Jian-Jiun Ding and Jiun-De Huang, "Image Compression by Segmentation and Boundary Description", Master‟s Thesis, National Taiwan University, Taipei, 2007. [7 ] Diffuse (2002). Guide to image compression, Retrieved October 23, 2003, from http:/www.diffuse.org/compress.html
Comparing the performance of compression technique is difficult unless identical data sets and performance measures are used. Some of these techniques are obtained good for certain applications like security technologies. REFERENCES [1 ] Subramanya A, “Image Compression Technique,” Potentials IEEE, Vol. 20, Issue 1 pp 19-23, Feb-March 2001. [2 ] Ming Yang & Nikolaos Bourbakis ,“An Overview of Lossless Digital Image Compression Techniques,” Circuits & Systems, 2005 48thMidwest Symposium ,vol. 2 IEEE ,pp 1099-1102,7 – 10 Aug, 2005. [3 ] Wen Shiung Chen, en- HuiYang & Zhen Zhang, “ A New Efficient Image Compression Technique with Index- Matching Vector Quantization,” Consumer Electronics, IEEE Transactions, Vol. 43, Issue 2, pp 173- 182, May 1997.
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