On a high performance image compression technique

We introduce an optimal approach to colour image compression using a new scan method. We propose efficient methods to increase the compression ratio for colour images by dividing the colour image into non-overlapping blocks and applying a different compression ratio for these blocks depending on the classification of blocks into edge and non-edge blocks. In an edge block (a region that contains important information) the compression ratio is reduced to prevent loss of information, while in a non-edge block (a smooth region which does not have important information), a high compression ratio is used. The new proposed scan is used instead of the zigzag scan. A particular implementation of this approach was tested, and its performance was quantified using the peak signal-to-noise ratio. Numerical results indicated general improvements in visual quality for colour image coding

An efficient color image compression technique

We present a new image compression method to improve visual perception of the decompressed images and achieve higher image compression ratio. This method balances between the compression rate and image quality by compressing the essential parts of the image-edges. The key subject/edge is of more significance than background/non-edge image. Taking into consideration the value of image components and the effect of smoothness in image compression, this method classifies the image components as edge or non-edge. Low-quality lossy compression is applied to non-edge components whereas high-quality lossy compression is applied to edge components. Outcomes show that our suggested method is efficient in terms of compression ratio, bits per-pixel and peak signal to noise ratio

The Digital Transformation Effects in Distance Education in Light of the Epidemics (COVID-19) in Egypt

On the relatively rare occasions when disaster forces schools and universities to close for a prolonged period of time, e- learning has helped fill the gap in instruction. In this paper, we study the role of digital transformation in e-learning systems in light of the global conditions resulting from the epidemics (COVID-19) in Egypt. Therefore, we focus on the importance of distance education at several factors, trying to assess the staffs response and students to new education methods and assess the distance education experience in Egyptian universities. Where the study set that various staff excited to utilize this method, unlike some of the students, who did not to accept because of their knowledge lacking

New Efficient Method for Coding Color Images

In this paper a novel color image compression technique for efficient storage and delivery of data is proposed. The proposed compression technique started by RGB to YCbCr color transformation process. Secondly, the canny edge detection is used to classify the blocks into edge and non-edge block. Each color component Y, Cb, and Cr compressed by discrete cosine transform (DCT) process, quantizing and coding step by step using adaptive arithmetic coding. Our technique is concerned with the compression ratio, bits per pixel and peak signal to noise ratio, and produc better results than JPEG and more recent published schemes (like, CBDCT-CABS and MHC). The provided experimental results illustrate the proposed technique is efficient and feasible in terms of compression ratio, bits per pixel and peak signal to noise ratio

ON CLUSTER VALIDITY INDEXES IN FUZZY AND HARD CLUSTERING ALGORITHMS FOR IMAGE SEGMENTATION

This paper addresses the issue of assessing the quality of the clusters found by fuzzy and hard clustering algorithms. In particular, it seeks an answer to the question on how well cluster validity indexes can automatically determine the appropriate number of clusters that represent the data. The paper surveys several key existing solutions for cluster validity in the domain of image segmentation. In addition, it suggests two new indexes. The first one is based on Akaike’s information criterion (AIC). While AIC was devoted to other domains such as statistical estimation of model fitting, it is implemented here for the first time as a validation index. The second index is developed from the well-established idea of cross-validation. The existing and new indexes are evaluated and compared on several synthetic images corrupted with noise of varying levels and volumetric MR data. Index Terms—clustering, cluster validity, fuzzy clustering, image segmentation. 1