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Register a new userStereoscopic Image Compression using Disparity Estimation between Images and 2D Digital Curvelet Transformation
ضغط الصور المجسمة بتقدير الفرق بين الصورتين و استخدام التحويل الانحنائي الرقمي ثنائي البعد
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يعتبر ضغط الصور أحد أهم فروع معالجة الصورة الرقمية حيث يُعنى بتقليل حجم الصور الملتقطة لتوفير المساحة المخصصة لها على أقراص التخزين و تسهيل عملية نقلها و إرسالها. يُقدِّم هذا البحث طريقة جديدة لضغط الصور المجسمة بالاعتماد على ثلاثة خوارزميات أولها المقارنة بين الصورتين المشكلتين للمنظر المجسم و الاستفادة من خاصية التشابه الكبير بينهما و ترميز الفرق بين الصورتين عوضاً عن ترميز الصورة بشكل كامل، و ثانيها بتقليل الفائض بين عناصر الصورة (Pixels) باستخدام التحويل الانحنائي الرقمي ثنائي البعد حيث نستفيد من قدرة هذا التحويل الكبيرة على تمثيل الانحناءات داخل الصورة بأقل عدد من المعاملات ليتم تكميتها و إزالة المعاملات غير المرغوبة و الحصول على عدد قليل من المعاملات الحاوية على أغلب تفاصيل الصورة، و آخرها باستخدام ترميز هوفمان و الاستفادة من خاصية عدم الفقد التي يتميز بها حيث يمكن ترميز الصورة و تقليل حجم بياناتها دون أن يحصل أي تشويه بالصورة أو فقدان أي جزء من هذه الصورة. كما يتم تقييم أداء خوارزمية البحث المقترح باستخدام معيار نسبة ضغط الصورة (Compression Ratio) أي نسبة عدد البتات الممثلة للصورة بعد الضغط إلى عدد البتات الممثلة للصورة الأصلية قبل الضغط، و كذلك معيار جودة الصورة (PSNR) أي مدى تشابه الصورة المستعادة مع الصورة الأصلية، و معيار متوسط مربعات الأخطاء (MSE) أي مقدار الخطأ في الصورة المستعادة، حيث ينبغي الحصول على أقل قيمة لنسبة ضغط الصور مع أعلى قيمة لجودة الصورة بأقل قيمة للأخطاء.
Image compression is one of the most important branches of digital image processing. It reduces the size of the captured images and minimizes the storage space on the drivers to speed up the transferring and transmission. In this paper we will present a new approach for compressing stereo images based on three algorithms; the first one is comparing the two images that perform the stereoscopic view by noticing the great similarities between them and encoding the difference between the two images instead of encoding the whole image. The second one is reducing the redundancy between the Pixels using a 2D Digital Curvelet Transformation so we can utilize the great ability to represent the curves in the image with minimum number of coefficients. Then quantize them and remove undesirable coefficient. The low number of coefficient contains most of image data. Last one is using Huffman Encoding and take advantage of the lossless property so we can encode image and reduce the size of data without getting any image distortion or lose any part of this image. The performance of the proposed algorithm evaluated using Compression Ratio standard which is the number of the image bits after compression to the number of the original image bits before compression. Also, Peak Signal to Noise Ratio standard (PSNR) which represent the similarity between the restored image and the original image. In final, the Mean Square Error standard (MSE) which represent the error between the restored image and original image. In conclusion, the main objective here is to get the lowest rate for image compression ratio with the highest value for the image quality PSNR at the lowest value of the errors MSE.
References used
Agarwal,A.,Compressing Stereo Images Using a Reference Image and the Exhaustive Block Matching Algorithm to Estimate Disparity between the Two Images. Vol. 32, Canada: International Journal of Advanced Science and Technology (IJAST), 2011
Aziz, T.,& Dolly,D.,Motion Estimation and Motion Compensated Video Compression Using DCT And DWT. Vol. 2,International Journal of Emerging Technology and Advanced Engineering (IJETAE),2012, 667-671
Dalvir, K.,&Kamaljit,K.,Huffman Based LZW Lossless Image Compression Using Retinex Algorithm. Vol. 2, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE),2013,3145-3151
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, on
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right .Both the left image ( reference image ) and the disparity map between left and right
images , were compressed using fractal compression . We applied both block matching
algorithm and Semi Global Method (SGM)to obtain the disparity map.
The left image and the depth map were reconstructed using fractal decompression
while the right image (target image ) was reconstructed using the reconstructed left image ,
disparity map and the error image between the original right image and the reconstructed
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The results were evaluated using quality objective measures which are MSE (Mean
Square Error ) and PSNR (Peak Signal to Noise Ratio) and efficiency objective measures
which are CR(Compression Ratio) and compression time . The results were compared
with JPEG compression of stereo pairs based on Discrete Cosine Transform DCT and
JPEG2000 compression of stereo pairs on stereo image based on Discrete Wavelet
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