Data Compression Techniques

Abstract

With the growth of multimedia and internet, compression techniques have become the thrust area in the fields of computers. Popularity of multimedia has led to integration of various types of computer data. Multimedia combines many data types like text, graphics, still images, animation, audio and video data. Many different data compression techniques currently exist for the compression of different types of data. Text compression requires regeneration for the original data from the compressed from in exactly the same from. Similarly, graphical data may also require the data to be same after decoding. Graphics, images, audio and video data require huge amount of storage. This not only require large storage capacity, but, the uncompressed from of these data types require higher band width for multimedia communication through computers or else the rate of data transfer is low making networks including Internet sluggish. Compression is one of the tools for better utilization of storage devices, resulting in saving of storage space. Current thesis describes various lossless and lossy data compression techniques, which includes Shannon-Fano coding, Huffman-coding algorithm, arithmetic coding, Runlength algorithm, LZ77 and LZW dictionary- based compression techniques and their implementation. Thesis also describes about the audio and image compression techniques.

Compression techniques may use more than one technique. For this, one method is applied first and encoded data so obtained undergoes another compression cycle using same or some other compression technique. With this higher compression rates can be achieved.