Data Compression Techniques

INTRODUCTION 

With the rapid development of technology with the support of software and hardware that increasingly facilitate widespread information quickly through the internet around the world. Information obtained can be sent easily via the internet as a the medium of communication for information technology experts. However, not all information can be sent easily. There is a large size that can hinder data transmission quickly and save on existing storage in the computer. 

To overcome the problem of information or data to be transmitted or transmitted can be done quickly than required a compression that can save storage and transmission of data to be done. Compression is the process of converting a data set into a code to save the need for storage and transmission of data making it easier to transmit a data. 

With the compression of a can save in terms of time and storage that exist in memory (storage). Many compression algorithm techniques can be performed and function properly such as the Huffman, Lempel Ziv Welch, Run Length Encoding, Tunstall, And Shannon Fano methods. The data process of data compression is shown in  below.

Compression is the reduction of a file size from a large size to a smaller file size. A compression will be done to facilitate the transmission of a file with a large size and contains many characters. The workings of a compression are by looking for patterns of repetition in the data and replace it with a certain sign. The type of compression has two methods, lossless compression and lossy compression. Lossless compression is the process of converting the original data with compressed data becomes more concise without reducing the loss of information. Lossy compression is the prose converts the original data into the compression data there are different values, but the value of this difference is considered does not reduce the essential information from the original data. 

Here is an explanation of the applicability that exists in a data compression :

 Compression for audio 

 Compression for text 

 Compression for video 

 Compression for image

Compression for audio 

Audio compression is one form of data compression option to shrink the size of the audio / video file by the method

- Overflow format: Vorbis, MP3;

- Unlimited format: FLAC; users: audio engineer, audiophiles 

Compression at the time of the creation of the audio file and at the moment distribution of the audio file. 

Audio compression constraints: 

- The development of sound recording is fast and diverse 

- Value of audio sample changes quickly .

Endless audio codecs have no issues in sound quality, usage can be focused on: 

- the speed of compression and decompression 

- The degree of compression 

- Support hardware and software 

Missing audio codecs available on: 

- Audio quality 

- compression factor 

- the speed of compression and decompression 

- Inherent latency of algorithm (essential for real-time streaming) 

- Support hardware and software 

Compression for text 

The decompression process returns the compressed file to the beginning of the text. Decompression results depend on the nature of the compression used, namely Lossless Compression or Lossy Compression. If a lossless compression technique has been performed on a text, the original text can be recovered correctly from the decompressed file (Sayood, 2001). Arithmetic encoding is a compression technique that is lossless compression. Lossy Compression results in the loss of some information, and decompression results can not produce exactly the same text as the original text (Sayood, 2001). 

Compression Ratio 

The Compression ratio shows the percentage of compression made against the original file. The compression ratio is derived from the equation: Difference in size Compression ratio = x 100% (1) original file.

The difference in size = original file - compression file (2) The higher the compression ratio the smaller the resulting compression file, the better compression result. 

Compression for video 

The video is a technology for capturing, recording, processing, transmitting, and rearranging moving images. Usually use celluloid film, electronic signal, or digital media. 

To digitize and store full-motion video clips for 10 minutes into a computer, it must transfer data in large quantities in a short time. To reproduce one frame of a 24-bit digital video component, computer data required is almost 1 MB, video not converted with layer for 30 seconds will meet the hard disk charged gigabyte. Full-size video and full-motion requires a computer that can transmit data of approximately 30 MB per second. Major technological bottlenecks can be overcome using digital video conversion schemes or codecs (coders / decoders). Codecs are algorithms used to convert (code) a video to be transmitted, then decoded directly for fast playback. Different codecs are optimized for different delivery methods (for example, from hard drives, CD-ROMs, or via the Web). The purpose of compression / video conversion is: minimization of bit rate in the digital representation of video signal, maintaining the desired signal quality level, minimizing codec complexity (coder and decoder-encoding and decoding), and delay or delay 

In other words video compression is one form of data compression that aims to shrink the video file size. Video compression refers to reducing the amount of data used to represent a digital video image, and is a combination of compression space of images and temporal compression of motion.

Compression for image

Graphic Interchange Format (GIF) created by Compuserve in 1987 to store multiple images with bitmap format into a file that is easy to change on a computer network. GIF is the oldest graphic file format on the Web. GIF supports up to 8-bit pixels, meaning a maximum number of colors 256 colors (28 = 256 colors), 4-pass interlacing, transparency and using variants of the Lempel-Ziv Welch (LZW) compression algorithm. 

There are two types of GIFs, among others: 

GIF87a: support with interlacing and capacity of multiple files. The technique was called GIF87 because in 1987 this standard was found and made standard.

GIF89a: is a continuation of the GIF87a specification and additions to transparency, text, and animation of text and graphics. Portable Network Graphic (PNG) format is designed to be better with the previous format that GIF has been legalized. PNG is designed for lossless algorithms for storing a bitmap image.PNG has a feature equation with GIF one of which is (multiple images), improving something eg (interlacing, compression) and adding the latest features. Support for Web where plug-ins can be made on web browsers. 

Joint Photographic Experts (JPEG, read jay-peg, [6]) are designed to compress some full-color or gray-scale of an original image, such as the original scene in the world. JPEGs work well on continuous tone images such as photographs or all the realm of art that permit the real; but not very good at the sharpness of images and the art of coloration such as writing, simple cartoons or drawings that use many lines. JPEGs already support for 24-bit color depth or equal to 16.7 million colors (224 = 16,777,216 colors) .The advantages of JPEG and type - they seem to be on the same steps as interlaced GIFs. JPEG 2000 is the most recent image compression technique. Jpeg 2000 is the development of Jpeg, which the number of bit errors are relatively low, rated, transmission and have a good quality compared with Jpeg. Jpeg 2000 applies lossy and lossless compression techniques. And the use of ROI coding (Region of interest coding). JPEG 2000 is designed for internet, scanning, digital photography, remote sensing, medical imagery, digital library and E-commerce.

Data Compression algorithms 

There are different types of algorithms used in data compression techniques:

• Shannon Fano Algorithm
• Run Length Encoding
• Lempel Ziv Welch
• Tunstall
• Huffman 

CONCLUSION 

Using the compression technique can reduce the number of file sizes. data that has a large size into a smaller size that can save storage in a computer. data compression can be implemented on a text, photo, and video data. various compression algorithm techniques have advantages and disadvantages in doing a compression.