A Survey OnDCT And Fuzzy Image Compression Algorithms

Image compression is the need of modern digital image processing devices to save larger images.By using the image compression algorithms which has better performance of compression. In this, implementing the compression technique which works on discrete cosine transform (DCT) image sub-block and use of fuzzy logic. There are so many techniques for compression but in this paper only present and compare between two techniques Dct and fuzzy block by block comparison. The main idea behind applying this algorithm is using all the coefficients of DCT sub-blocks and compare the results. The results of different number of coefficients are compared with the value of PSNR, MSE, compression percentage and compression time of algorithm. After comparison of techniques it is found to be efficient for visualisation, compressed file size and compression time. KeyTerms:DCT,FuzzyTransform,Quantization, Entropy,Compression ratio. Image compression plays an very important role in applications like televideo-conferencing, remote sensing and documents.Its main objective is to remove duplicacy or redundancy of images for storing and transmitting data in an efficient form.Uncompressed data require more space and time the main aim is to minimize the memory space of data.Two types-Lossy and lossless. In lossless techniques, reconstruction is possible from the compressed image.Inlossy technique some unnecessary information can be lost and original image cannot be recovered.There are many techniques in lossless such as run length encoding,huffman,arithmetic and LZW coding .In lossy ,it is DCT,DWT,FFT etc in transformation domain techniques used. Fuzzy is also efficient and reliable technique used for compression. A. Discrete Cosine transform The Discrete Cosine Transform (DCT) helps to separate the image into parts (or spectral subbands). The DCT is similar to the Discrete Fourier Transform as it transforms a signal or image or converts from the spatial domain to the frequency domain. A discrete cosine transform (DCT) expresses a sum of cosine functions oscillating at different frequencies. DCTs are important to many applications in real life and from lossy compression of audio (e.g. MP3) and images( e.g. JPEG) in which spectral value used for the numerical solution of partial differential equations. The use of cosine instead of sine functions is critical in these for compression,and cosine functions are much moreefficient . Fewer are needed to approximate orlessing the value of a typical signal whereas for differential equations, the cosines choses boundary conditions. The most commonly used variants of discrete cosine transform is the type-II DCT, which is oftenly called simple "the DCT"; its inverse, the type-III DCT, is often called "the inverse DCT" or "the IDCT". Two related transforms are the discrete sine transforms (DST), which is same as DFT of real and odd number functions, and the modified discrete cosine transforms (MDCT), which is based on a DCT of overlapping data.