Image compression analysis using multistage vector quantization based on discrete wavelet transform

Brifcani, Adnan Mohsin Abdulazeez; Al-Bamerny, Jwan Najeeb

doi:10.1109/icm2cs.2010.5706717

Cited by 11 publications

(11 citation statements)

References 10 publications

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“…The MSE of N × M size image is given by Equation 3 (Mohammad et al, 2017). PSNR is a geometric name, for the ratio between the maximum possible power of a signal and the power of vitiation noise that affects the accuracy (Brifcani and Al-Bamerny, 2010;Yamini, 2018). MSE (3) and PSNR (4) analysis shown in Table 1.…”

Section: Experimental Results Analysismentioning

confidence: 99%

Image Watermarking Using All Phase Discrete Cosine Biorthogonal Transform in Selected Pixel Blocks

Khalifa

Saeed

2020

Polytechnic j.

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In the past decade, a transform called all phase discrete cosine biorthogonal transform (APDCBT) appeared in the field of digital image processing. It is mainly used to solve the negativity found in discrete cosine transform (DCT), especially in a low bit rate. In this paper, the APDCBT is employed for watermark insertion based on selected regions of an image. The insertion is depending on the homogeneity of each part of the image. To determine the extent of homogeneity, there are two criteria: Mean and standard deviation are applied for the intensity of the image. Medium frequency bands of APDCBT in image pixel blocks are used to hold the embedded watermark. Then, the transform is inversed to obtain the resultant watermarked image. The robustness of APDCBT against watermark removal attacks is tested and the experimental results showed the superiority of the APDCBT over traditional DCT in the watermark embedding system. More specifically, when the LSB reset attack applied. For instance, when the contrast adjustment attack applied, the average of normalized cross-correlation (NCC) values of extracted watermark images with the original watermark, it was 0.992 for the proposed method. This is a promised result, if it is compared with the NCC of the DCT method, which was 0.423. The proposed method can be used for copyright protection purpose.

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Section: Experimental Results Analysismentioning

confidence: 99%

Image Watermarking Using All Phase Discrete Cosine Biorthogonal Transform in Selected Pixel Blocks

Khalifa

Saeed

2020

Polytechnic j.

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“…Relevant preprocessing on images should be carried out to remove or adjust noises through strategies such as localization, normalization, hair removal, image smoothing, color correction, removal of the vignette, and adjustment of contrast. Greater accuracy would be obtained with an appropriate combination of preprocessing tasks [13,[36][37][38].…”

Section: Skin Lesion Images Diagnoses Steps 1 Preprocessingmentioning

confidence: 99%

Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

Saeed

Zeebaree

2021

JASTT

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Skin cancer is among the primary cancer types that manifest due to various dermatological disorders, which may be further classified into several types based on morphological features, color, structure, and texture. The mortality rate of patients who have skin cancer is contingent on preliminary and rapid detection and diagnosis of malignant skin cancer cells. Limitations in current dermoscopic images, including shadow, artifact, and noise, affect image quality, which may hamper detection effort. Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection. In this paper, the authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification.

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“…indicates random permutation of first integer numbers 1: n, and f(m) represents which dimension of 's should learn from . As dissent to Φij in original ABC technique, generate random numbers where all numbers are between [0,2]. This modification makes the potential search space around EAk.…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

“…Because they can be so easily modified, copied and illegally attacked by anyone, new technology is needed to secure them. Digital watermarking is one of the most common and efficient approaches that is considered as an instrument for providing the copyright defense of digital content and has been proposed as a valid solution for this problem [1][2][3][4]. The watermarking is based on embedding a signature or more information into the host signal for identifying the copyright ownership [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Robust watermarking scheme based LWT and SVD using artificial bee colony optimization

Abdulazeez

Zeebaree

Hajy

et al. 2021

IJEECS

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<span>This paper presents a watermarking scheme for grayscale images, in which lifting wavelet transform and singular value decomposition are exploited based on multi-objective artificial bee colony optimization to produce a robust watermarking method. Furthermore, for increasing security encryption of the watermark is done prior to the embedding operation. In the proposed scheme, the actual image is altered to four sub-band over three levels of lifting wavelet transform then the singular value of the watermark image is embedded to the singular value of LH sub-band of the transformed original image. In the embedding operation, multiple scaling factors are utilized on behalf of the single scaling element to get the maximum probable robustness without changing watermark lucidity. Multi-objective artificial bee colony optimization is utilized for the determination of the optimal values for multiple scaling components, which are examined against various types of attacks. For making the proposed scheme more secure, the watermark is encrypted chaotically by logistic chaotic encryption before embedding it to the host (original) image. The experimental results show excellent imperceptibility and good resiliency against a wide range of image processing attacks.</span>

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Image compression analysis using multistage vector quantization based on discrete wavelet transform

Cited by 11 publications

References 10 publications

Image Watermarking Using All Phase Discrete Cosine Biorthogonal Transform in Selected Pixel Blocks

Image Watermarking Using All Phase Discrete Cosine Biorthogonal Transform in Selected Pixel Blocks

Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

Robust watermarking scheme based LWT and SVD using artificial bee colony optimization

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