Design and Optimization of the VLSI Architecture for Discrete Cosine Transform Used in Image Compression

Presentation of images plays a significant role in today\u27s information exchange. Numerous applications that have been introduced in last few years, such as video teleconferencing, HDTV, wirephoto, fax, computer tomography, interactive visualization, multimedia and other, are based on image presentation and distribution. procedures. Disadvantage of using digital images in these applications is enormous amount of space needed for image storage. For example, a 1024 x 1024 color image with 24 bits per pixel requires 3.15 M bytes in the raw form. It is obvious that efficient handling of images is possible only with the introduction of data compression techniques. Data compression is the reduction or elimination of redundancy in order to achieve savings in storage and communication costs. Data compression techniques can be classified in many ways, but the most common classification defines two basic categories: lossless and lossy. In lossless methods, the exact original data is recovered while in lossy methods only a close approximation of the original data can be obtained. Lossless methods are used in applications where no loss of information is allowed, such as text compression, medical imaging, and similar. Lossy methods are mostly used in image and audio compression where designers or users can select the quality of the restored data. Disadvantage of lossless methods is that the compression ratio is relatively small and runs between 3:1 and 4:1, while lossy methods can achieve compression ratios of up to 100:1. Even with compression ratios of that magnitude, due to the large volume of data, real-time operation of image using applications requires development of very high speed implementation cf image compression/decompression techniques