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DCT-Based Image Feature Extraction and Its Application in Image Self-Recovery and Image Watermarking

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DCT-Based Image Feature Extraction and Its Application in Image Self-Recovery and Image Watermarking

Hamid, Mohamed (2016) DCT-Based Image Feature Extraction and Its Application in Image Self-Recovery and Image Watermarking. Masters thesis, Concordia University.

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Abstract

Feature extraction is a critical element in the design of image self-recovery and watermarking algorithms and its quality can have a big influence on the performance of these processes. The objective of the work presented in this thesis is to develop an effective methodology for feature extraction in the discrete cosine transform (DCT) domain and apply it in the design of adaptive image self-recovery and image watermarking algorithms.
The methodology is to use the most significant DCT coefficients that can be at any frequency range to detect and to classify gray level patterns. In this way, gray level variations with a wider range of spatial frequencies can be looked into without increasing computational complexity and the methodology is able to distinguish gray level patterns rather than the orientations of simple edges only as in many existing DCT-based methods.
The proposed image self-recovery algorithm uses the developed feature extraction methodology to detect and classify blocks that contain significant gray level variations. According to the profile of each block, the critical frequency components representing the specific gray level pattern of the block are chosen for encoding. The code lengths are made variable depending on the importance of these components in defining the block’s features, which makes the encoding of critical frequency components more precise, while keeping the total length of the reference code short. The proposed image self-recovery algorithm has resulted in remarkably shorter reference codes that are only 1/5 to 3/5 of those produced by existing methods, and consequently a superior visual quality in the embedded images. As the shorter codes contain the critical image information, the proposed algorithm has also achieved above average reconstruction quality for various tampering rates.
The proposed image watermarking algorithm is computationally simple and designed for the blind extraction of the watermark. The principle of the algorithm is to embed the watermark in the locations where image data alterations are the least visible. To this end, the properties of the HVS are used to identify the gray level image features of such locations. The characteristics of the frequency components representing these features are identifying by applying the DCT-based feature extraction methodology developed in this thesis. The strength with which the watermark is embedded is made adaptive to the local gray level characteristics. Simulation results have shown that the proposed watermarking algorithm results in significantly higher visual quality in the watermarked images than that of the reported methods with a difference in PSNR of about 2.7 dB, while the embedded watermark is highly robustness against JPEG compression even at low quality factors and to some other common image processes.
The good performance of the proposed image self-recovery and watermarking algorithms is an indication of the effectiveness of the developed feature extraction methodology. This methodology can be applied in a wide range of applications and it is suitable for any process where the DCT data is available.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Hamid, Mohamed
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:15 August 2016
Thesis Supervisor(s):Wang, Chunyan
Keywords:Image Feature Extraction, Self-Recovery, Self-Embedding, Watermarking, Adaptive Encoding, Adaptive Embedding, HVS, DCT
ID Code:981491
Deposited By: Mohamed Hamid
Deposited On:08 Nov 2016 14:52
Last Modified:18 Jan 2018 17:53

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