17-04-2014, 05:01 PM
Image Compression Using Fast 2-D DCT Technique
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Abstract
Image Compression is a method, which reduces the size of the data to reduce the amount of
space required to store the data. The Discrete cosine transform (DCT) is a method for transforms a signal
or image from spatial domain to frequency component. It is a widely used technique in image
compression. In this paper, we present a lossless discrete cosine transform (DCT) compression technique
for two-dimensional images are proposed. In the several scenarios, the utilization of the proposed
technique for image compression resulted in comparable or better performance, when compared to the
different modes of the lossless JPEG standard.
INTRODUCTION
Image compression techniques have been designed to manipulate the statistical redundancy present within real
world images. Among the emerging standards are MPEG for compression of motion video, JPEG for
compression of images and CCITT H.261 (also known as Px64), for compression of video telephony and
teleconferencing.
All three of these standards employ a basic technique known as the discrete cosine transform (DCT), which is
developed by Ahmed, Natarajan, and Rao [1974]. It is a lossless compression technique. The DCT is usually
applied to reduce spatial redundancy in order to achieve good compression performance. Some of the
applications of two-dimensional DCT technique involve image compression and compression of individual
video frames. DCT is also useful for transferring multidimensional data from spatial domain to frequency
domain, where different operations, like spread spectrum, data compression, data watermarking can be
performed in performed manner. The JPEG process is a widely used form of lossy image compression that centers
on the Discrete Cosine Transform. DCT and Fourier transforms convert images from spatial-domain to
frequency-domain to decorrelate pixels. The JPEG is used for both color and black and-white images.
SOME BASIC COMPRESSION METHODS
The JPEG compression: Joint Photographic Expert Group (JPEG) which is commonly used method of
compression for photographic images. JPEG compression can be used in a variety of file formats:
• EPS-files
• EPS DCS-files
• JFIF-files
• PDF-files
Firstly the image is partitioned into non-overlapping 8*8 blocks. Then DCT is applied to each block to convert
the spatial domain gray level of pixels into coefficients in frequency domain. After the computation of DCT
coefficients, they are normalized according to a quantization table with different scales provided by the JPEG
standard computed by psycho visual evidence. The quantized coefficients are rearranged in a zigzag scan order
for further compressed by an efficient lossless coding algorithm such as run-length coding.
CONCLUSIONS
In this paper, there are 256 possible shades of gray in a black and white picture, and a difference of say 10 is
hardly obtrusive to the human eye. DCT takes advantage of redundancies of the data by grouping pixels with
similar frequencies. Thus we can also conclude that the difference between original and decompressed image
goes on decreasing as there is in increase in image resolution at the same compression ratio. This image
compression schemes for images have been presented based on the 2-D DCT. The anticipating results obtained
relevant reconstructed image quality as well as preservation of significant image details, while on the other hand
accomplishing high compression rates.
High compression ratio and better image quality accomplished which is better than existing methods. This paper
has concentrated on development of efficient and effective algorithm for still image compression. Fast and
lossless compression algorithm using 2-D DCT is developed. Results show that reduction in encoding time with
little degradation in image quality compare to subsisting method. Compression ratio is also increased, while
comparing the proposed method with other methods. Our future work involves improving image quality by
increasing PSNR value and lowering MSE value.