19-04-2017, 06:38 AM
Seminar report on configuration bitstream compression in FPGA
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Data Compression Techniques
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Seminar report on configuration bitstream compression in FPGA
24-04-2017, 09:44 AM
In signal processing, data compression, source coding, or bit rate reduction involves encoding information using fewer bits than the original representation. Compression may be loss or loss. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost on lossless compression. Lossy compression reduces bits by eliminating unnecessary or less important information. The process of reducing the size of a data file is called data compression. In the context of data transmission, it is referred to as source coding (coding performed on the data source before being stored or transmitted) as opposed to channel coding.
Compression is useful because it reduces the resources needed to store and transmit data. The computational resources are consumed in the compression process and, usually, in the inversion of the process (decompression). Data compression is subject to a compromise of spatiotemporal complexity. For example, a video compression scheme may require expensive hardware to decompress the video fast enough to be seen as it is being decompressed, and the option to decompress the entire video before viewing it may be inconvenient or require additional storage . The design of data compression schemes involves compromises between several factors, including the degree of compression, the amount of distortion introduced (when lossy data compression is used), and the computational resources required to compress and decompress the data.
Compression techniques
There are two categories of compression techniques used with digital graphics, lossy and lossless. While each uses different techniques to compress files, they both have the same goal. Find duplicate data in the chart and use a much more compact data representation.
Lossy and Lossless each have several methods that are used by different file formats and achieve different results. Therefore, not all lossless or lossless formats will use the same methods. It is beyond the scope of this Unit to look at these methods in detail so that they are not evaluated. The unit titled Digital Imaging: Bitmaps covers compression methods in more detail. If you are a little confused about this, the following may help:
• Lossy compression methods include DCT (Discreet Cosine Transform), Vector Quantisation, and Huffman coding
• Lossless compression methods include Run Length Encoding (RLE), string table compression, LZW (Lempel Ziff Welch), and zlib.
Compression methods are also known as algorithms, which are calculations that are used to compress files. Organizations that create file formats create their own algorithms and compete with each other to create the best format. You will see this in more detail later in the "File Format" section of this course.
Bitmaps consist of pixels having the same values. Think of a picture of a stage. There will be areas of pixels in the photo that all have the same value. There is no need to store each pixel. Algorithms look for redundant data, that is, repeat values that are not needed, in the graph and create a new representation of the data. This representation creates a smaller file size but keeps the relevant data to display the graph. Essentially, when you look at a compressed file, you should see no or little difference in the chart.
"CODEC (compressor / decompressor) is used to carry out the algorithm to save a file in a compressed format and open a compressed file.CODEC can be implemented in hardware or software Hardware CODECs are more expensive, but because they use dedicated chips Instead Of the CPU time of computers, are significantly more efficient. "
PC Multimedia & Web Handbook - Technology & Techniques, 2nd Edition by David Dick, Drumbreck Publishing, 2002
Lossy and lossless techniques use different algorithms to help achieve good quality graphics with smaller file sizes. The technique you choose depends on the type of chart you are working with.
Compression is useful because it reduces the resources needed to store and transmit data. The computational resources are consumed in the compression process and, usually, in the inversion of the process (decompression). Data compression is subject to a compromise of spatiotemporal complexity. For example, a video compression scheme may require expensive hardware to decompress the video fast enough to be seen as it is being decompressed, and the option to decompress the entire video before viewing it may be inconvenient or require additional storage . The design of data compression schemes involves compromises between several factors, including the degree of compression, the amount of distortion introduced (when lossy data compression is used), and the computational resources required to compress and decompress the data.
Compression techniques
There are two categories of compression techniques used with digital graphics, lossy and lossless. While each uses different techniques to compress files, they both have the same goal. Find duplicate data in the chart and use a much more compact data representation.
Lossy and Lossless each have several methods that are used by different file formats and achieve different results. Therefore, not all lossless or lossless formats will use the same methods. It is beyond the scope of this Unit to look at these methods in detail so that they are not evaluated. The unit titled Digital Imaging: Bitmaps covers compression methods in more detail. If you are a little confused about this, the following may help:
• Lossy compression methods include DCT (Discreet Cosine Transform), Vector Quantisation, and Huffman coding
• Lossless compression methods include Run Length Encoding (RLE), string table compression, LZW (Lempel Ziff Welch), and zlib.
Compression methods are also known as algorithms, which are calculations that are used to compress files. Organizations that create file formats create their own algorithms and compete with each other to create the best format. You will see this in more detail later in the "File Format" section of this course.
Bitmaps consist of pixels having the same values. Think of a picture of a stage. There will be areas of pixels in the photo that all have the same value. There is no need to store each pixel. Algorithms look for redundant data, that is, repeat values that are not needed, in the graph and create a new representation of the data. This representation creates a smaller file size but keeps the relevant data to display the graph. Essentially, when you look at a compressed file, you should see no or little difference in the chart.
"CODEC (compressor / decompressor) is used to carry out the algorithm to save a file in a compressed format and open a compressed file.CODEC can be implemented in hardware or software Hardware CODECs are more expensive, but because they use dedicated chips Instead Of the CPU time of computers, are significantly more efficient. "
PC Multimedia & Web Handbook - Technology & Techniques, 2nd Edition by David Dick, Drumbreck Publishing, 2002
Lossy and lossless techniques use different algorithms to help achieve good quality graphics with smaller file sizes. The technique you choose depends on the type of chart you are working with.
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