TIFF is, in principle, a very flexible format that can be lossless or lossy. The details of the image storage algorithm are included as part of the file. In practice, TIFF is used almost exclusively as a lossless image storage format that uses no compression at all. That means that no data is being lost in the compression , unlike a lossy format like JPG.
So you can rest easy: using TIFF compression will not degrade your image quality. It uses a combination of the LZ77 algorithm and Huffman coding. Lempel-Ziv-Welch LZW compression is a lossless compression algorithm that performs a limited analysis of data. Data compression is the process of modifying, encoding or converting the bits structure of data in such a way that it consumes less space on disk. It enables reducing the storage size of one or more data instances or elements.
Data compression is also known as source coding or bit-rate reduction. Arithmetic coding as a generalized change of radix In a positional numeral system the radix, or base, is numerically equal to a number of different symbols used to express the number. For example , in the decimal system the number of symbols is 10, namely 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9.
The LZ77 Compression Algorithm is used to analyze input data and determine how to reduce the size of that input data by replacing redundant information with metadata. Also known as Huffman encoding , an algorithm for the lossless compression of files based on the frequency of occurrence of a symbol in the file that is being compressed. Compression starts the second time a sequence is encountered.
The key point is that a sequence from the input file is not added to the code table until it has already been placed in the compressed file as individual characters codes 0 to This is important because it allows the uncompression program to reconstruct the code table directly from the compressed data, without having to transmit the code table separately. Figure shows a flowchart for LZW compression. When we say that the LZW algorithm reads the character "a" from the input file, we mean it reads the value: 97 expressed in 8 bits , where 97 is "a" in ASCII.
When we say it writes the character "a" to the encoded file, we mean it writes: 97 expressed in 12 bits. The variable, CHAR , holds a single character, i. In box 1 of Fig. Table shows this action in line 1. This is followed by the algorithm looping for each additional byte in the input file, controlled in the flow diagram by box 8. Each time a byte is read from the input file box 2 , it is stored in the variable, CHAR. An example of these actions is shown in lines 2 through 10 in Table , for the first 10 bytes of the example file.
That is, if a matching sequence is found in the table, no action should be taken before determining if there is a longer matching sequence also in the table. This flow of events is continued until there are no more characters in the input file. The program is wrapped up with the code corresponding to the current value of STRING being written to the compressed file as illustrated in box 9 of Fig.
A flowchart of the LZW uncompression algorithm is shown in Fig. Each code is read from the compressed file and compared to the code table to provide the translation. As each code is processed in this manner, the code table is updated so that it continually matches the one used during the compression. However, there is a small complication in the uncompression routine. This algorithm maintains a separate dictionary.
Suppose you once again want to compress the following string of text: the quick brown fox jumps over the lazy dog. In , Terry Welch was working on a compression algorithm for high-performance disk controllers.
He developed a rather simple algorithm that was based on the LZ78 algorithm and that is now called LZW. LZW compression replaces strings of characters with single codes. It does not do any analysis of the incoming text. Instead, it just adds every new string of characters it sees to a table of strings.
Compression occurs when a single code is output instead of a string of characters. The code that the LZW algorithm outputs can be of any arbitrary length, but it must have more bits in it than a single character.
The first codes when using eight-bit characters are by default assigned to the standard character set. On the other hand, Lossy compression reduces bits by removing unnecessary or less important information. So we need Data Compression mainly because: Attention reader! Uncompressed data can take up a lot of space, which is not good for limited hard drive space and internet download speeds.
While hardware gets better and cheaper, algorithms to reduce data size also help technology evolves. How can we fit a two-hour film on a 25 GB Blu-ray disc? The LZW algorithm is a very common compression technique. It is lossless, meaning no data is lost when compressing.
The algorithm is simple to implement and has the potential for very high throughput in hardware implementations. It is the algorithm of the widely used Unix file compression utility compress and is used in the GIF image format.
The Idea relies on reoccurring patterns to save data space. LZW is the foremost technique for general-purpose data compression due to its simplicity and versatility. How does it work? LZW compression works by reading a sequence of symbols, grouping the symbols into strings, and converting the strings into codes. Because the codes take up less space than the strings they replace, we get compression.
Characteristic features of LZW includes, LZW compression uses a code table, with as a common choice for the number of table entries. Codes in the code table are always assigned to represent single bytes from the input file. When encoding begins the code table contains only the first entries, with the remainder of the table being blanks.
Compression is achieved by using codes through to represent sequences of bytes. As the encoding continues, LZW identifies repeated sequences in the data and adds them to the code table.
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