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Decompressing PNG Image Data sion method to replace it The solution came in the form of a general-purpose compression library known as ZLIB ZLIB employs an LZ77-based compression process known as Deflate, which had its origins in the ZIP and PKZIP programs The compression source code within ZIP was too tightly bound to the application for general use, so JeanLoupe Gailly and Mark Adler created ZLIB to implement Deflate compression in a manner that can be used by other applications ZLIB has been used not only in PNG but in the GZIP archiving program as well Currently, Deflate is the only compression method supported by ZLIB, but the ZLIB format has provisions for other methods to be added in the future For this discussion we are going to describe ZLIB and Deflate only as they apply to PNG Not all settings that are valid in ZLIB/Deflate are legal when used in PNG files The source code examples in this and the following chapters contain implementations of the PNG subset of eflate.

LZ77 Compression The LZ77 process uses a sliding window to maintain a dictionary of recently processed text The compressed stream is a sequence of codes that are either literal values or commands for the decompressor to copy text from the window to the output stream An LZ77 decompressor reads each code in the compressed stream in sequence Codes that represent literal values are copied directly to the output stream Command codes are replaced in the output stream with text copied from the LZ window In either case, the LZ window is advanced so that the last character copied to the output stream is included in the window The big advantage of dictionary compression over Huffman coding is that compression can be done on the fly without having to process the entire stream, making it suitable for applications such as compression of data streams in a computer network Figure 141 contains a simplified example of LZ77 decompression using a 16-byte window The data consists of 7-bit ASCII text, so by using 1 bit to differentiate a literal value from a command, each code in the compressed stream can be encoded using 8 bits In this example copy commands are represented as <Offset:Length> where the offset is the number of bytes from the start of the LZ77 window and the length is the umber of bytes to copy In this example the first six codes are literal values that are copied to the output stream The seventh code copies two characters from the tenth position in the LZ Window ("A") to the output stream As new codes are read, the window fills up and text starts to become lost to the compression processes Notice that the final "MA" in the text could have been compressed into a code had that string not slid out of the window.

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A parallel system, whose interprocessor communication is based on message passing, typically performs interprocessor communication with a relatively expensive overhead Consequently, subtasks should be quite large so that the overhead becomes small when compared with the computation time of the tasks; otherwise the speedup of the parallel program is degraded A massively parallel system, with hundreds of processors, needs a high degree of parallelism in order to involve all processors in the execution of the program, which obviously cannot be achieved with only a few large tasks These two examples demonstrate the in uence of the system architecture on the subtask decomposition of a program Also, the programming paradigm can have a similar effect A centralized shared-memory system, which inherently supports ne grained parallelism, can also be programmed with a message passing paradigm Yet, when doing so, the same applies as for a message passing system; hence, coarser ranularity is indicated Earlier discussion showed that the decomposition of a program is a complex task that is strongly interwoven with other parts of the parallelization process As was mentioned at the beginning of this section, it is not possible to treat subtask decomposition separately from its context Nevertheless, this section gave an overview of the general subject and techniques and referred to the associated problems In the next section, subtask decomposition will be seen in practice for simple program examples.

The chunk data contains a keyword and a keyword value The chunk data format is shown in Table 137 The length of the Keyword field is determined by locating the N U L L (0) terminator This length may not be zero The length of the Text field is the length of the chunk data minus the length of the Keyword and the Terminator This length may be ero Line breaks within the text should be represented with a single <LINEFEED> character The PNG standard defines the keywords shown in Table 138 An encoder can use these or create new keywords; however, a decoder should use the predefined keywords when they are applicable to maximize portability Keywords are case sensitive. In Visual Studio .NET Using Barcode decoder for .Related: Data Matrix Generation Word , ASP.NET UPC-E Generator , ISBN Generator VB.NET

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Assume now that Eq (23) corresponds to the initial application speci cation of the parallelization process as described in Section 23 (see Figure 215) The rst step is to divide the equation into subtasks This can be done by considering every operation as one task For example, suppose the value 2 is assigned to a, a program can be ritten from Eq (23) with four tasks as presented in Example 1 JIS X 0510 Maker In NET Using Barcode maker for ASP Related: Excel Code 128 Generator , Print Code 39 C# , Generate Interleaved 2 of 5 C#.

Huffman Table Generation A PNG encoder can either use the fixed Huffman codes shown in Table 149 or generate Huffman codes based on usage frequencies It is simpler to implement fixed Huffman codes but there is obviously a penalty when it comes to compression Unless you are working with an application where compression speed is critical, there is really no reason to use fixed Huffman codes 6 covered Huffman coding as it applies to JPEG The same process with a few modifications will work with a Huffman encoder The differences between Huffman table generation in JPEG and PNG were listed in the previous chapter When we used Huffman coding in JPEG, we generated the Huffman table by making two nearly identical passes over the image data The first pass gathered usage frequencies After generating the Huffman tables from the usage frequencies, the second pass repeated the steps of the first pass except that the data was Huffman encoded Such a scheme can be used to encode PNG image data but there are a couple of significant drawbacks The main problem with having two nearly identical passes is the time required to compress an image The process of searching the LZ77 window for matching strings is significantly more processing intensive than is JPEG entropy encoding Performing PNG compression process wice lengthens the compression time noticeably A good solution to this problem is to store the literal/length and distance codes in a buffer A simple method for implementing such a buffer would be to. Code In Visual Basic .NET Using Barcode maker for .Related: Make ITF-14 Excel , QR Code Generating C# , C# PDF417 Generation

Vectr d/dt x = A * x + B * u Vector y = C * x + D * u x (x1, x2, ) is a vector of state variables, and u and y are vectors of system input and utput variables The matrices A, B, C, and D define the plant and.Once declared, the new function can be invoked in the experiment protocol or in a DYNAMIC program segment, say with. Printer In Visual Basic .NET Using Barcode maker for VS .Related: .NET WinForms ITF-14 Generating , Create EAN-8 Word , .NET ISBN Generator

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Using Barcode creation for Java Control to generate, create barcode image in ava applications.After we have read 4 bits the code value is less than or equal to the maximum value for length 4 This means we have read in the entire Huffman code Since the minimum code value of length 4 is 11002, 11012 is the second Huffman code of length 4 (1101 2 - 11002 = 1) The first value with a Huffman code of length 4 is the ourth value in the value array, so the value we are looking for is the fifth, which is M If you look back at Figure 61 you can see that this is correct.Conclusion In this chapter we introduced Huffman coding and covered it's implementation in JPEG compression Huffman coding is also used in PNG compression, and he basic method is the same in both formats In 14 we will cover the specific requirements for implementing Huffman coding in PNG Nelson (1992) has a more general description of Huffman coding Huffman (1952) contains its original description The JPEG standard JPEG (1994) is the source for the JPEG-specific requirements of Huffman coding The source code for this chapter on the accompanying CD-ROM contains classes for JPEG Huffman encoding (JpegHuffmanEncoder and JpegHuffmanDecoder) There is also an application called HUFFCOMP for compressing a file using Huffman coding, in which the command HUFFCOMP SOURCE-FILE COMPRESSED-FILE creates a compressed output file This application reduces the size of the King James Bible by about 44 percent The CD contains a corresponding application for expanding files, in which the command HUFFDECO COMPRESSED-FILE DESTINATION-FILE expands a file compressed with HUFFCOMP to its original state.Related: Printing UPC-A C# , Creating Interleaved 2 of 5 .NET WinForms , Create EAN-13 ASP.NET

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