Robust Image Compression Algorithm using Discrete Fractional Cosine Transform

The discrete fractional Fourier transform become paradigm in signal processing. This transform process the signal in joint time-frequency domain. The attractive and very important feature of DFrCT is an availability of extra degree of one free parameter that is provided by fractional orders and due to which optimization is possible. Less execution time and easy implementation are main advantages of proposed algorithm. The merit of effectiveness of proposed technique over existing technique is superior due to application of discrete fractional cosine transform by which higher compression ratio and PSNR are obtained without any artifacts in compressed images. The novelty of the proposed algorithm is no artifacts in compressed image along with good CR and PSNR. Compression ratio (CR) and peak signal to noise ratio (PSNR) are quality parameters for image compression with optimum fractional order.

A spatial image compression algorithm based on run length encoding

Image compression is vital for many areas such as communication and storage of data that is rapidly growing nowadays. In this paper, a spatial lossy compression algorithm for gray scale images is presented. It exploits the inter-pixel and the psycho-visual data redundancies in images. The proposed technique finds paths of connected pixels that fluctuate in value within some small threshold. The path is calculated by looking at the 4-neighbors of a pixel then choosing the best one based on two conditions; the first is that the selected pixel must not be included in another path and the second is that the difference between the first pixel in the path and the selected pixel is within the specified threshold value. A path starts with a given pixel and consists of the locations of the subsequently selected pixels. Run-length encoding scheme is applied on paths to harvest the inter-pixel redundancy. After applying the proposed algorithm on several test images, a promising quality vs. compression ratio results have been achieved.

Image Compression and Enlargement Algorithms

In some cases, there are problems associated with the compression and enlargement of images. The use of splines is quite effective in some cases. In this paper, a new image compression algorithm is presented. The features of increasing the size of an image when using local polynomial or non-polynomial splines are considered. The main method for enlarging an image is based on the use of splines of the second and third order of approximation. Polynomial and trigonometric splines are considered. To speed up the process of enlarging the image, we used the parallelization techniques

An Efficient Compression of Gray Scale Images using Wavelet Transform

The rapid development of technology and the standardization of digital photography have led to an explosive growth in digital image distribution and reproduction. The enhancement of storage capacity in computer disks and advancement in networking have not been able to keep pace with the demands of handling, storing and finally transmitting huge volume of image data. Only proper image compression technologies seem to offer a solution to this challenge. The importance of digital image compression in multimedia applications has inspired extensive research all over the world. The present study recommends a newly formulated algorithm by computing Discrete Wavelet Transform (DWT) in combination with thresholding and quadtree decomposition. Findings prove that the proposed technique is at par with EZW image compression algorithm in terms of quality performance at the same bit rate, and obviates the need for employing any other conventional standard image compression techniques.

Adaptive color space model based on dominant colors for image and video compression performance improvement

This paper describes the use of some color spaces in JPEG image compression algorithm and their impact in terms of image quality and compression ratio, and then proposes adaptive color space models (ACSM) to improve the performance of lossy image compression algorithm. The proposed ACSM consists of, dominant color analysis algorithm and YCoCg color space family. The YCoCg color space family is composed of three color spaces, which are YCcCr, YCpCg and YCyCb. The dominant colors analysis algorithm is developed which enables to automatically select one of the three color space models based on the suitability of the dominant colors contained in an image. The experimental results using sixty test images, which have varying colors, shapes and textures, show that the proposed adaptive color space model provides improved performance of 3 % to 10 % better than YCbCr, YDbDr, YCoCg and YCgCo-R color spaces family. In addition, the YCoCg color space family is a discrete transformation so its digital electronic implementation requires only two adders and two subtractors, both for forward and inverse conversions.