DOCSLIB.ORG

A Comparative Study of Lossless Compression Techniques

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Comparative Study of Lossless Compression Techniques International Journal of Advances in Electronics and Computer Science, ISSN: 2393-2835 Volume-4, Issue-2, Feb.-2017 http://iraj.in A COMPARATIVE STUDY OF LOSSLESS COMPRESSION TECHNIQUES 1J P SATI, 2M J NIGAM 1, 2 Indian Institute of Technology, Roorkee, India E-mail: [email protected], [email protected] Abstract- As we are dealing with more and more digital data, several compression techniques are developed for increasing need to store more data in lesser memory. Compressing can save storage capacity, speed file transfer, and decrease costs for storage hardware and network bandwidth. This paper intends to provide the performance analysis of lossless compression techniques with respect to various parameters like compression ratio, compression factor, saving percentage, compression and de-compression timeetc. It provides the relevant data about variations in parameters as well as describes the possible causes for it. The simulation results of image compression are achieved in MATLAB R2009a. The paper focuseson thede- compression time and the reasons for differences in comparison. Keywords- Run length Coding (RLE), Huffman, Arithmetic, Lempel-ziv-welch (LZW), Compression Ratio. I. INTRODUCTION compression. In this method, thecompressed image is not same as the original image; there is some amount Data compression is a technique that transforms the of information loss in the image. data from one representation to another new In lossy compression, much information can be compressed (in bits) representation, which contains simply discarded away from image data/audio the same information but with smallest possible size data/video data and when they are uncompressed the [13]. The size of data is reduced by removing the data will still be of acceptable quality. excessive or redundant information. The data is stored or transmitted at reduced storage and/or II. LOSSLESS COMPRESSION METHODS communication costs. Compressing a file to half of its original size is equivalent to doubling the capacity of Commonly used lossless compression techniques are the storage medium. It may then become feasible to Run Length Encoding (RLE), Huffman Coding, store the data at a higher level of the storage Arithmetic coding and Lempel-Ziv-Welch (LZW) hierarchy and reduce the load on the input/output channels of the system. 2.1 Run length coding Run-length encoding (RLE) is a data compression algorithm that is supported by most bitmap file formats, such as TIFF, BMP, and PCX. RLE is suited for compressing any type of data regardless of its information content, but the content of the data will affect the compression ratio achieved by RLE. RLE is both easy to implement and quick to execute, making it a good alternative to either using a complex compression algorithm or leaving your image data uncompressed. Fig1: Compression and de-compression process RLE works by reducing the physical size of a repeating string of characters. This repeating string, There are two compression techniques named as called a run, is typically encoded into two bytes. The Lossless and Lossy compression. first byte represents the number of characters in the In lossless compression scheme the reconstructed run and is called the run count. In practice, an image is same as the input image. Lossless image encoded run may contain 1 to 128 or 256 characters; compression techniques first convert the images into the run count usually contains as the number of the image pixels. Then processing is done on characters minus one (a value in the range of 0 to 127 eachsingle pixel. The First step includes prediction of or 255). The second byte is the value of the character next image pixel value from the in the run, which is in the range of 0 to 255, and is neighbourhoodpixels. In the second stage the called the run value. difference between the predicted value and the actual intensity of thenext pixel is coded using different Arun of 15 A’s would normally require 15 bytes to encoding methods. store: Lossy compression technique provides higher AAAAAAAAAAAAAAA compression ratio as compared to lossless A Comparative Study of Lossless Compression Techniques 45 International Journal of Advances in Electronics and Computer Science, ISSN: 2393-2835 Volume-4, Issue-2, Feb.-2017 http://iraj.in The same string after RLE would require only two Implementation of Arithmetic codingwas carried out bytes: 15A.This compression technique is useful for in MATLAB R2009a [12]. The stepsfor executing the monochrome images or images having the same code are as follows:- background pixels. Implementation of Run-length Convert the image into greyscale image. encoding is carried out in MATLAB R2009a. The Read the greyscale image and store all the stepsfor executing the code are as follows:- intensity values as a single row vector. Convert the image into greyscale image. Convert the matrix into binary form and Read the grey scale image and rearrange arrange all the bits in binary stream representing the data of image as single row vector. same image. Convert all intensities values to binary state Encode the entire stream using arithmetic & obtain a binary stream representation of image. encoding algorithm. Count consecutive 1’s and 0’s appeared in a Calculate the compression ratio as the ratio sequence and stored as run length encoded sequence. of original image and size of Arithmetic coded Reconstruct the original image. sequence. Calculate the compression ratio as the ratio of original image and size of run lengthencoded 2.4 Lempel-Ziv-Welch (LZW) coding sequence. LZW compression algorithm is dictionary based algorithm. This means that instead of tabulating 2.2 Huffman coding character counts and building trees (as for Huffman It is a variable length coding technique which is encoding), LZW encodes data by referencing a coded for the symbols based on their probabilities. dictionary. It representsthe variable length symbols Symbols are generated based on the pixels in an with fixed length codes. The original version of this image. On the basis of the frequency of occurrence of method was created by Lempel and Ziv in 1978 the symbols, bits are assigned to it. Less bits are (LZ78) and was further refined by Welch in 1984, assigned to the symbols that occur more frequently hence the LZW acronym. while more number of bits are assigned to the Dictionary based coding scheme are of two types, symbols that occur less frequently. In Huffman Static and Adaptive. In Static Dictionary based coding the generated binary code of any symbol is coding, dictionary size is fixed during encoding and not the prefix of the code of any other symbol [3] [5]. decoding processes and in Adaptive Dictionary based Implementation of Huffman coding was carried out in coding; dictionary size is updated and reset when it is MATLAB R2009a. The stepsfor executing the code completely filled. Since images are used as data, are as follows:- static coding suits for the compression job with Convert the image into greyscale image. minimum delay [3] [6] [7]. Read the greyscale image and convert the Implementation of LZW encoding was carried out in array into singlerow vector. MATLAB R2009a [6][7]. The stepsfor executing the From the grey scale image, form a Huffman code are as follows:- encoding tree using probability of symbols in the Convert the image into greyscale image. image. Read an image and arrange all the intensity Encode each symbol independently using values in single row vector. the Huffman encoding tree. Convert all the values in binary form and Reconstruct the original image by achieve a single row binary representation. decompressing it using Huffman decoding. Initialize the dictionary with basic symbols 1 Calculate the compression ratio as the ratio and 0. of original image and size of Huffman coded Start encoding & decoding based on search sequence. & find method. Add any new word found in dictionary and encode the sequence. 2.3 Arithmetic encoding If dictionary is completely filled, continue Arithmetic coding is also a variable length coding using same dictionary. technique.In this technique, the entire symbols Calculate the compression ratio as the ratio generated from the pixels is converted into a single of original image and size of encoded sequence. floating point number also termed as binary fraction. In arithmetic coding technique, a tag is generated for III. EVALUATION AND COMPARISON the sequence which is to be encoded. This tag signifies the given binary fraction and becomes the 3.1 Performance Parameters unique binary code for the sequence. This unique Depending on the nature of the application there are binary code generated for a given sequence of certain various criteria to measure the performance of a length is not dependent on the entire length of compression algorithm.Following are some sequence [1] [4] [10]. measurements parameters used to evaluate the performances of lossless algorithms. A Comparative Study of Lossless Compression Techniques 46 International Journal of Advances in Electronics and Computer Science, ISSN: 2393-2835 Volume-4, Issue-2, Feb.-2017 http://iraj.in Compression Ratio is the ratio between the size of the Table 1: Compressionratio compressed file and the size of the source file. size after compression compression ratio = size before compression Compression Factor is the inverse of the compression ratio. That is the ratio between the size of the source file and the size of the compressed file. sizebeforecompression compressionfactor = sizeaftercompression Saving Percentage calculates the shrinkage of the Table 2: Compression factor source file as a percentage. sizeaftercompression savingpercentage = 1 − sizebeforecompression Bits per pixel is the number of bits per pixel used in the compressed representation of the image. 8 bitsperpixel = compression ratio Table 3: Saving Percentage Along with the above parameters compression and de-compression time, are also used to measure the effectiveness. Compression and De-Compression Time Time taken for the compression and decompression should be considered separately.
Load more

Recommended publications
  • Data Compression: Dictionary-Based Coding 2 / 37 Dictionary-Based Coding Dictionary-Based Coding
    Dictionary-based Coding already coded not yet coded search buffer look-ahead buffer cursor (N symbols) (L symbols) We know the past but cannot control it. We control the future but... Last Lecture Last Lecture: Predictive Lossless Coding Predictive Lossless Coding Simple and effective way to exploit dependencies between neighboring symbols / samples Optimal predictor: Conditional mean (requires storage of large tables) Affine and Linear Prediction Simple structure, low-complex implementation possible Optimal prediction parameters are given by solution of Yule-Walker equations Works very well for real signals (e.g., audio, images, ...) Efficient Lossless Coding for Real-World Signals Affine/linear prediction (often: block-adaptive choice of prediction parameters) Entropy coding of prediction errors (e.g., arithmetic coding) Using marginal pmf often already yields good results Can be improved by using conditional pmfs (with simple conditions) Heiko Schwarz (Freie Universität Berlin) — Data Compression: Dictionary-based Coding 2 / 37 Dictionary-based Coding Dictionary-Based Coding Coding of Text Files Very high amount of dependencies Affine prediction does not work (requires linear dependencies) Higher-order conditional coding should work well, but is way to complex (memory) Alternative: Do not code single characters, but words or phrases Example: English Texts Oxford English Dictionary lists less than 230 000 words (including obsolete words) On average, a word contains about 6 characters Average codeword length per character would be limited by 1
    [Show full text]
  • A Survey Paper on Different Speech Compression Techniques
    Vol-2 Issue-5 2016 IJARIIE-ISSN (O)-2395-4396 A Survey Paper on Different Speech Compression Techniques Kanawade Pramila.R1, Prof. Gundal Shital.S2 1 M.E. Electronics, Department of Electronics Engineering, Amrutvahini College of Engineering, Sangamner, Maharashtra, India. 2 HOD in Electronics Department, Department of Electronics Engineering , Amrutvahini College of Engineering, Sangamner, Maharashtra, India. ABSTRACT This paper describes the different types of speech compression techniques. Speech compression can be divided into two main types such as lossless and lossy compression. This survey paper has been written with the help of different types of Waveform-based speech compression, Parametric-based speech compression, Hybrid based speech compression etc. Compression is nothing but reducing size of data with considering memory size. Speech compression means voiced signal compress for different application such as high quality database of speech signals, multimedia applications, music database and internet applications. Today speech compression is very useful in our life. The main purpose or aim of speech compression is to compress any type of audio that is transfer over the communication channel, because of the limited channel bandwidth and data storage capacity and low bit rate. The use of lossless and lossy techniques for speech compression means that reduced the numbers of bits in the original information. By the use of lossless data compression there is no loss in the original information but while using lossy data compression technique some numbers of bits are loss. Keyword: - Bit rate, Compression, Waveform-based speech compression, Parametric-based speech compression, Hybrid based speech compression. 1. INTRODUCTION -1 Speech compression is use in the encoding system.
    [Show full text]
  • XAPP616 "Huffman Coding" V1.0
    Product Obsolete/Under Obsolescence Application Note: Virtex Series R Huffman Coding Author: Latha Pillai XAPP616 (v1.0) April 22, 2003 Summary Huffman coding is used to code values statistically according to their probability of occurence. Short code words are assigned to highly probable values and long code words to less probable values. Huffman coding is used in MPEG-2 to further compress the bitstream. This application note describes how Huffman coding is done in MPEG-2 and its implementation. Introduction The output symbols from RLE are assigned binary code words depending on the statistics of the symbol. Frequently occurring symbols are assigned short code words whereas rarely occurring symbols are assigned long code words. The resulting code string can be uniquely decoded to get the original output of the run length encoder. The code assignment procedure developed by Huffman is used to get the optimum code word assignment for a set of input symbols. The procedure for Huffman coding involves the pairing of symbols. The input symbols are written out in the order of decreasing probability. The symbol with the highest probability is written at the top, the least probability is written down last. The least two probabilities are then paired and added. A new probability list is then formed with one entry as the previously added pair. The least symbols in the new list are then paired. This process is continued till the list consists of only one probability value. The values "0" and "1" are arbitrarily assigned to each element in each of the lists. Figure 1 shows the following symbols listed with a probability of occurrence where: A is 30%, B is 25%, C is 20%, D is 15%, and E = 10%.
    [Show full text]
  • Image Compression Through DCT and Huffman Coding Technique
    International Journal of Current Engineering and Technology E-ISSN 2277 – 4106, P-ISSN 2347 – 5161 ©2015 INPRESSCO®, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Image Compression through DCT and Huffman Coding Technique Rahul Shukla†* and Narender Kumar Gupta† †Department of Computer Science and Engineering, SHIATS, Allahabad, India Accepted 31 May 2015, Available online 06 June 2015, Vol.5, No.3 (June 2015) Abstract Image compression is an art used to reduce the size of a particular image. The goal of image compression is to eliminate the redundancy in a file’s code in order to reduce its size. It is useful in reducing the image storage space and in reducing the time needed to transmit the image. Image compression is more significant for reducing data redundancy for save more memory and transmission bandwidth. An efficient compression technique has been proposed which combines DCT and Huffman coding technique. This technique proposed due to its Lossless property, means using this the probability of loss the information is lowest. Result shows that high compression rates are achieved and visually negligible difference between compressed images and original images. Keywords: Huffman coding, Huffman decoding, JPEG, TIFF, DCT, PSNR, MSE 1. Introduction that can compress almost any kind of data. These are the lossless methods they retain all the information of 1 Image compression is a technique in which large the compressed data. amount of disk space is required for the raw images However, they do not take advantage of the 2- which seems to be a very big disadvantage during dimensional nature of the image data.
    [Show full text]
  • Arxiv:2004.10531V1 [Cs.OH] 8 Apr 2020
    ROOT I/O compression improvements for HEP analysis Oksana Shadura1;∗ Brian Paul Bockelman2;∗∗ Philippe Canal3;∗∗∗ Danilo Piparo4;∗∗∗∗ and Zhe Zhang1;y 1University of Nebraska-Lincoln, 1400 R St, Lincoln, NE 68588, United States 2Morgridge Institute for Research, 330 N Orchard St, Madison, WI 53715, United States 3Fermilab, Kirk Road and Pine St, Batavia, IL 60510, United States 4CERN, Meyrin 1211, Geneve, Switzerland Abstract. We overview recent changes in the ROOT I/O system, increasing per- formance and enhancing it and improving its interaction with other data analy- sis ecosystems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly to improve experiment’s software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically in- creased during the LHC era. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly: there are sig- nificant trade-offs between the increased CPU cost for reading and writing files and the reduce storage space. 1 Introduction In the past years LHC experiments are commissioned and now manages about an exabyte of storage for analysis purposes, approximately half of which is used for archival purposes, and half is used for traditional disk storage. Meanwhile for HL-LHC storage requirements per year are expected to be increased by factor 10 [1]. arXiv:2004.10531v1 [cs.OH] 8 Apr 2020 Looking at these predictions, we would like to state that storage will remain one of the major cost drivers and at the same time the bottlenecks for HEP computing.
    [Show full text]
  • The Strengths and Weaknesses of Different Image Compression Methods Samuel Teare and Brady Jacobson Lossy Vs Lossless
    The Strengths and Weaknesses of Different Image Compression Methods Samuel Teare and Brady Jacobson Lossy vs Lossless Lossy compression reduces a file size by permanently removing parts of the data that may be redundant or not as noticeable. Lossless compression guarantees the original data can be recovered or decompressed from the compressed file. PNG Compression PNG Compression consists of three parts: 1. Filtering 2. LZ77 Compression Deflate Compression 3. Huffman Coding Filtering Five types of Filters: 1. None - No filter 2. Sub - difference between this byte and the byte to its left a. Sub(x) = Original(x) - Original(x - bpp) 3. Up - difference between this byte and the byte above it a. Up(x) = Original(x) - Above(x) 4. Average - difference between this byte and the average of the byte to the left and the byte above. a. Avg(x) = Original(x) − (Original(x-bpp) + Above(x))/2 5. Paeth - Uses the byte to the left, above, and above left. a. The nearest of the left, above, or above left to the estimate is the Paeth Predictor b. Paeth(x) = Original(x) - Paeth Predictor(x) Paeth Algorithm Estimate = left + above - above left Distance to left = Absolute(estimate - left) Distance to above = Absolute(estimate - above) Distance to above left = Absolute(estimate - above left) The byte with the smallest distance is the Paeth Predictor LZ77 Compression LZ77 Compression looks for sequences in the data that are repeated. LZ77 uses a sliding window to keep track of previous bytes. This is then used to compress a group of bytes that exhibit the same sequence as previous bytes.
    [Show full text]
  • Annual Report 2016
    ANNUAL REPORT 2016 PUNJABI UNIVERSITY, PATIALA © Punjabi University, Patiala (Established under Punjab Act No. 35 of 1961) Editor Dr. Shivani Thakar Asst. Professor (English) Department of Distance Education, Punjabi University, Patiala Laser Type Setting : Kakkar Computer, N.K. Road, Patiala Published by Dr. Manjit Singh Nijjar, Registrar, Punjabi University, Patiala and Printed at Kakkar Computer, Patiala :{Bhtof;Nh X[Bh nk;k wjbk ñ Ò uT[gd/ Ò ftfdnk thukoh sK goT[gekoh Ò iK gzu ok;h sK shoE tk;h Ò ñ Ò x[zxo{ tki? i/ wB[ bkr? Ò sT[ iw[ ejk eo/ w' f;T[ nkr? Ò ñ Ò ojkT[.. nk; fBok;h sT[ ;zfBnk;h Ò iK is[ i'rh sK ekfJnk G'rh Ò ò Ò dfJnk fdrzpo[ d/j phukoh Ò nkfg wo? ntok Bj wkoh Ò ó Ò J/e[ s{ j'fo t/; pj[s/o/.. BkBe[ ikD? u'i B s/o/ Ò ô Ò òõ Ò (;qh r[o{ rqzE ;kfjp, gzBk óôù) English Translation of University Dhuni True learning induces in the mind service of mankind. One subduing the five passions has truly taken abode at holy bathing-spots (1) The mind attuned to the infinite is the true singing of ankle-bells in ritual dances. With this how dare Yama intimidate me in the hereafter ? (Pause 1) One renouncing desire is the true Sanayasi. From continence comes true joy of living in the body (2) One contemplating to subdue the flesh is the truly Compassionate Jain ascetic. Such a one subduing the self, forbears harming others. (3) Thou Lord, art one and Sole.
    [Show full text]
  • An Optimized Huffman's Coding by the Method of Grouping
    An Optimized Huffman’s Coding by the method of Grouping Gautam.R Dr. S Murali Department of Electronics and Communication Engineering, Professor, Department of Computer Science Engineering Maharaja Institute of Technology, Mysore Maharaja Institute of Technology, Mysore [email protected] [email protected] Abstract— Data compression has become a necessity not only the 3 depending on the size of the data. Huffman's coding basically in the field of communication but also in various scientific works on the principle of frequency of occurrence for each experiments. The data that is being received is more and the symbol or character in the input. For example we can know processing time required has also become more. A significant the number of times a letter has appeared in a text document by change in the algorithms will help to optimize the processing processing that particular document. After which we will speed. With the invention of Technologies like IoT and in assign a variable string to the letter that will represent the technologies like Machine Learning there is a need to compress character. Here the encoding take place in the form of tree data. For example training an Artificial Neural Network requires structure which will be explained in detail in the following a lot of data that should be processed and trained in small paragraph where encoding takes place in the form of binary interval of time for which compression will be very helpful. There tree. is a need to process the data faster and quicker. In this paper we present a method that reduces the data size.
    [Show full text]
  • Arithmetic Coding
    Arithmetic Coding Arithmetic coding is the most efficient method to code symbols according to the probability of their occurrence. The average code length corresponds exactly to the possible minimum given by information theory. Deviations which are caused by the bit-resolution of binary code trees do not exist. In contrast to a binary Huffman code tree the arithmetic coding offers a clearly better compression rate. Its implementation is more complex on the other hand. In arithmetic coding, a message is encoded as a real number in an interval from one to zero. Arithmetic coding typically has a better compression ratio than Huffman coding, as it produces a single symbol rather than several separate codewords. 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, a fraction n where (0.0 ≤ n < 1.0) Arithmetic coding is a lossless coding technique. There are a few disadvantages of arithmetic coding. One is that the whole codeword must be received to start decoding the symbols, and if there is a corrupt bit in the codeword, the entire message could become corrupt. Another is that there is a limit to the precision of the number which can be encoded, thus limiting the number of symbols to encode within a codeword. There also exist many patents upon arithmetic coding, so the use of some of the algorithms also call upon royalty fees. Arithmetic coding is part of the JPEG data format.
    [Show full text]
  • Image Compression Using Discrete Cosine Transform Method
    Qusay Kanaan Kadhim, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.9, September- 2016, pg. 186-192 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 5.258 IJCSMC, Vol. 5, Issue. 9, September 2016, pg.186 – 192 Image Compression Using Discrete Cosine Transform Method Qusay Kanaan Kadhim Al-Yarmook University College / Computer Science Department, Iraq [email protected] ABSTRACT: The processing of digital images took a wide importance in the knowledge field in the last decades ago due to the rapid development in the communication techniques and the need to find and develop methods assist in enhancing and exploiting the image information. The field of digital images compression becomes an important field of digital images processing fields due to the need to exploit the available storage space as much as possible and reduce the time required to transmit the image. Baseline JPEG Standard technique is used in compression of images with 8-bit color depth. Basically, this scheme consists of seven operations which are the sampling, the partitioning, the transform, the quantization, the entropy coding and Huffman coding. First, the sampling process is used to reduce the size of the image and the number bits required to represent it. Next, the partitioning process is applied to the image to get (8×8) image block. Then, the discrete cosine transform is used to transform the image block data from spatial domain to frequency domain to make the data easy to process.
    [Show full text]
  • Lossless Compression of Audio Data
    CHAPTER 12 Lossless Compression of Audio Data ROBERT C. MAHER OVERVIEW Lossless data compression of digital audio signals is useful when it is necessary to minimize the storage space or transmission bandwidth of audio data while still maintaining archival quality. Available techniques for lossless audio compression, or lossless audio packing, generally employ an adaptive waveform predictor with a variable-rate entropy coding of the residual, such as Huffman or Golomb-Rice coding. The amount of data compression can vary considerably from one audio waveform to another, but ratios of less than 3 are typical. Several freeware, shareware, and proprietary commercial lossless audio packing programs are available. 12.1 INTRODUCTION The Internet is increasingly being used as a means to deliver audio content to end-users for en­ tertainment, education, and commerce. It is clearly advantageous to minimize the time required to download an audio data file and the storage capacity required to hold it. Moreover, the expec­ tations of end-users with regard to signal quality, number of audio channels, meta-data such as song lyrics, and similar additional features provide incentives to compress the audio data. 12.1.1 Background In the past decade there have been significant breakthroughs in audio data compression using lossy perceptual coding [1]. These techniques lower the bit rate required to represent the signal by establishing perceptual error criteria, meaning that a model of human hearing perception is Copyright 2003. Elsevier Science (USA). 255 AU rights reserved. 256 PART III / APPLICATIONS used to guide the elimination of excess bits that can be either reconstructed (redundancy in the signal) orignored (inaudible components in the signal).
    [Show full text]
  • The H.264 Advanced Video Coding (AVC) Standard
    Whitepaper: The H.264 Advanced Video Coding (AVC) Standard What It Means to Web Camera Performance Introduction A new generation of webcams is hitting the market that makes video conferencing a more lifelike experience for users, thanks to adoption of the breakthrough H.264 standard. This white paper explains some of the key benefits of H.264 encoding and why cameras with this technology should be on the shopping list of every business. The Need for Compression Today, Internet connection rates average in the range of a few megabits per second. While VGA video requires 147 megabits per second (Mbps) of data, full high definition (HD) 1080p video requires almost one gigabit per second of data, as illustrated in Table 1. Table 1. Display Resolution Format Comparison Format Horizontal Pixels Vertical Lines Pixels Megabits per second (Mbps) QVGA 320 240 76,800 37 VGA 640 480 307,200 147 720p 1280 720 921,600 442 1080p 1920 1080 2,073,600 995 Video Compression Techniques Digital video streams, especially at high definition (HD) resolution, represent huge amounts of data. In order to achieve real-time HD resolution over typical Internet connection bandwidths, video compression is required. The amount of compression required to transmit 1080p video over a three megabits per second link is 332:1! Video compression techniques use mathematical algorithms to reduce the amount of data needed to transmit or store video. Lossless Compression Lossless compression changes how data is stored without resulting in any loss of information. Zip files are losslessly compressed so that when they are unzipped, the original files are recovered.
    [Show full text]