Lossless Data Compression Techniques: A Case Study Meena kumari M.Tech CSE Deptt.BPSMV khanpur kalan (sonipat) E-Mail: meena.boora24@gmail.com
Abstract:
Compression is a technique of representing the information in a compact form. It is a most requirement technique for compressed the computerized application. It reduces the amount of data and also decrease transfer time. Data compression used in files storage and distributed system. There are two type of data compression “lossy” and “lossless” but this paper examines
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Assign a 0 and 1 code word to the two branches of the tree on the path from the root. After the Huffman tree, the method creates a prefix code for each node from the alphabet by traversing the tree
Ex:
B 15
C 10
A 20
D 5 Building a Huffman code tree short the list in order the characters from highest to lowest frequency of occurrence as follows:
20 15 10 5
A B C D
Figure -2 Huffman algorithm
Tracing down the tree gives the "Huffman codes", with the shortest codes assigned to the characters with the greatest frequency: A 0 B 10 C 110 D 111
Advantages and disadvantages:
Huffman’s algorithm is generating less redundancy codes compared to other algorithms. It uses coding in text, image, video compression, and conferencing system such as, JPEG, MPEG-2, and MPEG-4 etc [9] .Compression ratio and memory space is low.
2.4 Arithmetic Coding:
It is more advanced than Huffman coding. Arithmetic coding changes the method of replacing each bit with a codeword. There for it replaces the string of input data with a single floating point number as an output. The main purpose of the algorithm is to given an interval to each potential bit data [10].
Basic arithmetic coding iteration: range = high -
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Arup Kumar Bhattacharjee1, Tanumon Bej2, Saheb Agarwal3,”compression study of lossless data compression algorithm for text data” IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 11, Issue 6, pp1-5 (May. - Jun. 2013), 4. Anmol Jyot Maan “Analysis and Comparison of Algorithms for Lossless Data Compression” International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, 1-8, Number 3 (2013), pp. 139-146
5. Mohammed Al-laham1 & Ibrahiem M. M. El Emary2 “Comparative Study Between Various Algorithms of Data Compression Techniques” Proceedings of the World Congress on Engineering and Computer Science 2007 WCECS 2007, pp1-11, October 24-26, 2007, San Francisco, USA
6. Ajit Singh and Yogita Bhatnagar” Enhancement of Data Compression Using Incremental Encoding” International Journal of Scientific & Engineering Research, Volume 3, Issue 5,pp1-5 May-2012 1 ISSN 2229-5518
7. Amarjit Kaur Navdeep Singh Sethi Harinderpal Singh“A Review on Data Compression Techniques”International Journal of Advanced Research Computer Science and Software Engineering, Volume5, Issue 1, pp1-5, January 2015 ISSN: 2277 128X
8. Senthil Shanmugasundaram, Robert Lourdusamy, ”A comparative study of text compression algorithm” International Journal of Wisdom Based Computing,Vol. 1(3),pp 1-9 December
In the demonstration, we only add a 2-bit (a0,b0) with another 2-bit binary (a1,b1) together. In the first picture on the last section of the Figures/Graphs, we added 01 and 00 together and the result was 01; the first LED on the right turns ON and the second LED is stilled turned OFF. In the last two pictures, 01 + 00 = 10 (the second LED turned on and the first one was OFF) and 11 + 11 = 110; the LED indicate this long results, the carry out was 1 and the sum (s1, s0) was 1 and 0. This circuit allowed us to understand the idea of full adders ICs which can be used in electronics like computers, tablets, etc. to add binaries together to perform specific functions and desired
1. C1 then performs a permutation on vector [Y] and sends it to C2. C2 decrypts the vector and informs C1 where the distinct bit is located. By performing reverse permutation C1 knows precisely where the bit flip occurs and the two key bits that must be compared 2.
Some of the MS SQL Datatypes Affected By Compression Some of the datatype that don’t yield any row level compression benefit are tinyint, smalldatetime, date, time, varchar, text, nvarchar,xml. MS SQL Server Page Compression can be applied to tables, table partitions, indexes and index partitions. This compression technique can be viewed as an enhanced version of Dictionary Encoding discussed in [4]. The following two figures illustrate the effects of page compression on an uncompressed
For example; M = 4 bits, N = 16 bits If P(j) = 1 (Propagation); then Group(j) will be skipped X(j): m-bits of group (j) Y(j): m-bits of group (j) Cin(j): Carry in to group(j) Cout(j) = Cin(j+1): Carry out of group(j) = Carry in to next group(j+1) (j): Group(j) consisting of m-bits numbers to add Fig 5.14: Carry Skip Adder Block diagram Table 5.3: Carry out Cases Table Case Xi Yi Xi + Yi Ci+1 Comment Ci = 0 Ci = 1 1 0 0 0
Basic Operators Besides assigning a variable an initial value, we can also perform the usual mathe- matical operations on variables. Basic operators in Python include +, -, *, /, //, % and ** which represent addition, subtraction, multiplication, division, floor divi- sion, modulus and exponent respectively. Example: Suppose x = 5, y = 2 Addition:
$A$ is a set of conditions $C_{i,L_j},{i,j}inmathbb{N}$ at the same hierarchical level $L_j$. Only one condition $Cin A$ can be extit{true} at the same time and no state transition without being specified by a condition is possible. If condition $C_{i,L_j}$ is not extit{true} any more (due to the proceeding of the assembly operation), there is a fallback to state $S_{j,L_i}$ and all conditions are evaluated to determine the current substate. An exemplary decomposition tree containing different hierarchical levels, multiple states per level and conditions for state transition is given by Fig.~
\subsection{Creation of Matching Entries ({\it MakeMatchingEntries'})} \label{sec:make-matching} \label{sec:match} Procedure ${\it MakeMatchingEntries'}$ takes a decoding entry set and a pattern as input and outputs a newly created set that contains the entries that match the input pattern. Note that not every entry in the newly created set is the same entry in the input entries because the exclusion conditions were modified. The exclusion conditions that are invalidated by the input pattern are removed from the exclusion condition set of output entries. In addition, when all the matching patterns are invalidated, the unmatching patterns are expanded to their opcode patterns. Procedure ${\it MakeMatchingEntries'}$ consists of the following
It is a logic optimization used to reduce the area of complex logic in integrated circuits, thus making the circuit more efficient, which is
(struct hfnode**)malloc(min_tree->hfm_cp * sizeof(struct hfnode*)); int i; struct node *temp2; temp2 = input; i=0; while(temp2!=NULL) { info[i]=temp2->data; freq[i]=temp2->freq; temp2=temp2->next; i++; } for(i=0;inodes[i] = new_node(info[i],freq[i]); min_tree->length = length; int sz,count; sz = min_tree->length - 1; for (count = (sz - 1) / 2; count >= 0; --count) min_tree_construct(min_tree, count); while (! (min_tree->length == 1)) { l1 = min_take(min_tree); r1 = min_take(min_tree); tmp = new_node(' ', l1->prob + r1->prob); tmp->l = l1; tmp->r = r1; insertmin_tree(min_tree, tmp); } rt
Add (+) # 2) subtract (-) # 3) Multiply (*) # 4) Divide (/) def getFirstNumber(self): #Gets first number from user self.number1 = self.__checkNumber("Please enter the first number: ") def getSecondNumber(self): #Gets second number from user self.number2 =
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