تطوير ادوات جديدة في نظام مودل == Developing New Tools in Moodle System
تصنيف اورام الدماغ باستخدام الشبكات العصبية الاصطناعية
تسريع ضغط الصور الكسوري باستخدام تقنيات مطورة == Fast Fractal Image Compression using Adaptive Techniques
Author name:
حيدر عباس محسن
Supervisor name:
حازم باقر طاهر العلي
Abstract:
Fractal Image Compression (FIC) is one of the lossy techniques. In order for fractal compression image to be encoded is partitioned into non - overlapping blocks called ranges. From this range pool creates a new array called domain pool, the data of domain is array produced from taking an average of every four (2x2) adjacent elements in the range array, the domain is divided into overlapping blocks. Each block in range pool should be matched with every large number of blocks in the domain pool in order to find minimum error and recorded its best IFS approximate. The decoder process applies the determined IFS transformations on any initial image, and the process is repeated many times until reaching the attractor. In this thesis, it presents reducing the long encoding time of FIC without making significant sacrifice in the quality. In addition to the traditional fractal compression method, four IFS coding methods have been tested.The first method is speed up encoding phase by using moment features of the domain blocks with threshold , this method it compute the moment features of domain blocks only , during the matching process, compares the ratio factor of domain block with threshold, then compute the scaling (s), offset (o) coefficients and minimum error between range and domain blocks, and storing the optimal IFS coding , the results of this method with lenna image (4x4) block size, (the encoding time = 50.29 sec), (compression ration= 4.57 ) ,(PSNR=24.04), (RTR=33.42 ). The second method is speed up encoding phase by using the entropy technique of the domain blocks only , during the matching process, compares entropy value of domain block with threshold, and storing optimal IFS coding , the results of lenna image(4x4) block size, (the encoding time = 95.47sec), (compression ration= 4.47 ) ,(PSNR= 24.08) and (RTR= 17.60) . In the third method it merges the entropy technique with moment features of domain blocks only , during the matching process, compares entropy value and moments ratio factor of domain blocks with threshold and storing IFC coding, the compression results of lenna image (4x4)block size , (encoding time = 100.90 sec), (compression ration=4.47) ,(PSNR= 24.11) and (RTR=16.66) , the flaw in this method is the encoding time of 16x16 partitioning more than 4x4 and 8x8 partitioning. In the fourth method speed up encoding phase by use moment features of the range and domain blocks, in this method it computes the moment features of range and domain blocks, during the matching process, if the moments ratio factor of range and domain blocks are equal then compute scale(s),offset(o) and error (R) .storing best IFS coding. the results of lenna image(4x4) (encoding time = 114.02 sec), (compression ration= 4.47) ,(PSNR= 24.43), (RTR=14.74), the flaw in this method is the encoding time of 16x16 partitioning more than 4x4 and 8x8 partitioning. Matlab R2008a was used as a program to designed and implemented to achieve the coding and testing tasks. The environment used for our tests is a single PC with processor (core i3 CPU 2.53GHz) and 4.00 GB RAM .
خوارزمية موازنة الاحمال المقترحة في الحوسبة السحابية == A Proposed Load Balancing Algorithm in Cloud Computing
مخطط يربط المفتاح بالقياسات الحيوية لحماية انظمة المعلومات == A Biometric Key Binding Scheme for Information Systems Protection
