Development of edge detection algorithm for blocky compressed image

Nowadays, compressed image is a very useful way to transfer or store data because of its smaller size in comparison to the original image. However, if the compression ratio is high, the produced image may be corrupted by blocky artifacts. In many applications that require only the detection of the i...

Full description

Saved in:
Bibliographic Details
Main Authors: Sari, Suhaila, Sapuan, Mohammad Idris, Roslan, Hazli, Tukiran, Zarina, Ab Rahman, Tasiransurini, Md Taujuddin, Nik Shahidah Afifi, Yusuf , Siti Idzura
Format: Conference or Workshop Item
Published: 2013
Subjects:
Online Access:http://eprints.uthm.edu.my/4520/
http://eprints.uthm.edu.my/4520/1/DEVELOPMENT_OF_EDGE_DETECTION_ALGORITHM.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Nowadays, compressed image is a very useful way to transfer or store data because of its smaller size in comparison to the original image. However, if the compression ratio is high, the produced image may be corrupted by blocky artifacts. In many applications that require only the detection of the image edges, it is difficult to distinguish the edges, fine details and noise. This task is more difficult in compressed image, since the generated blocky artifacts have almost the same properties as the true edges. Therefore, in this study we develop an algorithm that could detect only the edges from a compressed image. For horizontal direction, two variables are calculated for every pixel in the odd-numbered rows of the image. The two variables are the discontinuities in horizontal pixel intensity gradient at the neighbouring pixels, which will be compared with a threshold value. If both values are higher than the threshold value, the corresponding pixel is classified as edge. Detection in vertical direction is performed in the same manner. The detection results for horizontal and vertical directions are generated in two new black and white images, respectively. The generated new images are then combined to produce the final detected image. From our simulation, we found that this detection method has successfidly detected edges without including the blocky artifacts and fine details.