Most of today’s techniques encrypt all of the image data, which consumes a tremendous amount of time and computational payload. This work introduces a selective image encryption technique that encrypts predetermined bulks of the original image data in order to reduce the encryption/decryption time and thecomputational complexity of processing the huge image data. This technique is applying a compression algorithm based on Discrete Cosine Transform (DCT). Two approaches are implemented based on color space conversion as a preprocessing for the compression phases YCbCr and RGB, where the resultant compressed sequence is selectively encrypted using randomly generated combined secret key.The results showed a significant reduction in image quality degradation when applying the system based on YCbCr over RGB, where the compression ratio was raised in some of the tested images to 50% for the same Peak Signal to Noise Ratio (PSNR). The usage of 1-D DCT reduced the transform time by 47:1 times comparedto the same transform using 2-D DCT. The values of the adaptive scalar quantization parameters were reduced to the half for the luminance (Y band) to preserve the visual quality, while the chrominance (Cb and Cr bands) were quantized by the predetermined quantization parameters. In the hybrid encoder horizontal zigzag,block scanning was applied to scan the image. The Detailed Coefficient (DC) coefficients are highly correlated in this arrangement- where DC are losslessly compressed by Differential Pulse Coding Modulation (DPCM) and theAccumulative Coefficients (AC) are compressed using Run Length Encoding (RLE). As a consequence, for the compression algorithm, the compression gain obtained was up to 95%. Three arrays are resulted from each band (DC coefficients, AC values, and AC runs), where the cipher is applied to some or all of those bulksselectively. This reduces the encryption decryption time significantly, where encrypting the DC coefficients provided the second best randomness and the least encryption/decryption time recorded (3 10-3 sec.) for the entire image. Although the compression algorithm consumes time but it is more efficient than the savedencryption time.

There are many events that took place in Al Mosul province between 2013 and 2018. These events led to many changes in the area under study. These changes involved a decrease in agricultural crops and water due to the population leaving the area. Therefore, it is imperative that planners, decision-makers, and development officials intervene in order to restore the region's activity in terms of environment and agriculture. The aim of this research is to use remote sensing (RS) technique and geographic information system (GIS) to detect the change that occurred in the mentioned period. This was achieved through the use of the ArcGIS software package for the purpose of assessing the state of lands of agricultural crops and forests. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) were adopted in the current calculations. This can help the decision-maker take the necessary measures to avoid the problems caused by the emergency events. The results obtained through this research showed that the region had rate changes in farms, water, and forests of about 1%, as it was found that there was a decrease in the level of the Tigris River and an increase in the area of ​​ carrot crop farms. Also, the results indicated a decrease in areas of agricultural crops in specific regions, while they increased in others.

Human skin detection, which usually performed before image processing, is the method of discovering skin-colored pixels and regions that may be of human faces or limbs in videos or photos. Many computer vision approaches have been developed for skin detection. A skin detector usually transforms a given pixel into a suitable color space and then uses a skin classifier to mark the pixel as a skin or a non-skin pixel. A skin classifier explains the decision boundary of the class of a skin color in the color space based on skin-colored pixels. The purpose of this research is to build a skin detection system that will distinguish between skin and non-skin pixels in colored still pictures. This performed by introducing a metric that measures the distances of pixel colors to skin tones. Results showed that the YCbCr color space performed better skin pixel detection than regular Red Green Blue images due to its isolation of the overall energy of an image in the luminance band. The RGB color space poorly classified images with wooden backgrounds or objects. Then, a histogram-based image segmentation scheme utilized to distinguish between the skin and non-skin pixels. The need for a compact skin model representation should stimulate the development of parametric models of skin detection, which is a future research direction.

Color image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images and obtaining a low bit rate by compressing the matrix resulting from the scalar quantization process (reducing the number of bits from 24 to 8 bits) using displacement coding and then compressing the remainder using the Mabel ZF algorithm Welch LZW. The proposed methodology maintains the quality of the reconstructed image. Macroscopic and quantitative experimental results on technical color images show that the proposed methodology gives reconstructed images with a high PSNR value compared to standard image compression techniques.

Information is an essential and valuable object in all systems. The more information you have about your issue, the better you can conform to the world around you. Moreover, information recognizes companies and provides influence that helps one company be more effective than another. So, protecting this information using better security controls and providing a high level of access to authorized parties becomes an urgent need. As a result, many algorithms and encryption techniques have been developed to provide a high level of protection for system information. Therefore, this paper presents an enhancement to the Blowfish algorithm as one of the cryptography techniques. Then it proposes an enhancement for increasing efficiency and secrecy for this algorithm, which are the main criteria for these modifications. In this paper, the main modification made for the Blowfish algorithm is altering the S-box according to the principles of the block cipher (OFB). The results were approved using hamming distance and avalanche effect. The proposed algorithm presents advantages on several points, including reducing the required time for the encryption and decryption processes and preventing transmission errors from perpetuating all data. Also, we can conclude that the modified Blowfish algorithm can be used for all text encryption systems because of its flexibility (unlimited input text size and expanding key size).