scholarly journals Chimera: A New Efficient Transform for High Quality Lossy Image Compression

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 378
Author(s):  
Walaa Khalaf ◽  
Ahmad Saeed Mohammad ◽  
Dhafer Zaghar
Keyword(s):  
Image Compression ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Suggested Approach ◽  
Simple Slope ◽  
Image Pixels ◽  
Karhunen Loeve Transform ◽  
The Cost ◽  
Index Measure

A novel scheme is presented for image compression using a compatible form called Chimera. This form represents a new transformation for the image pixels. The compression methods generally look for image division to obtain small parts of an image called blocks. These blocks contain limited predicted patterns such as flat area, simple slope, and single edge inside images. The block content of these images represent a special form of data which be reformed using simple masks to obtain a compressed representation. The compression representation is different according to the type of transform function which represents the preprocessing operation prior the coding step. The cost of any image transformation is represented by two main parameters which are the size of compressed block and the error in reconstructed block. Our proposed Chimera Transform (CT) shows a robustness against other transform such as Discrete Cosine Transform (DCT), Wavelet Transform (WT) and Karhunen-Loeve Transform (KLT). The suggested approach is designed to compress a specific data type which are the images, and this represents the first powerful characteristic of this transform. Additionally, the reconstructed image using Chimera transform has a small size with low error which could be considered as the second characteristic of the suggested approach. Our results show a Peak Signal to Noise Ratio (PSNR) enhancement of 2.0272 for DCT, 1.179 for WT and 4.301 for KLT. In addition, a Structural Similarity Index Measure (SSIM) enhancement of 0.1108 for DCT, 0.051 for WT and 0.175 for KLT.

scholarly journals Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network

2021 ◽  
Vol 11 (17) ◽  
pp. 7803
Author(s):  
Yooho Lee ◽  
Sang-hyo Park ◽  
Eunjun Rhee ◽  
Byung-Gyu Kim ◽  
Dongsan Jun
Keyword(s):  
Image Compression ◽  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Network Architectures ◽  
Blocking Artifacts ◽  
Feature Extractor ◽  
Index Measure ◽  
Compression Artifacts

Since high quality realistic media are widely used in various computer vision applications, image compression is one of the essential technologies to enable real-time applications. Image compression generally causes undesired compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a densely cascading image restoration network (DCRN), which consists of an input layer, a densely cascading feature extractor, a channel attention block, and an output layer. The densely cascading feature extractor has three densely cascading (DC) blocks, and each DC block contains two convolutional layers, five dense layers, and a bottleneck layer. To optimize the proposed network architectures, we investigated the trade-off between quality enhancement and network complexity. Experimental results revealed that the proposed DCRN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed joint photographic experts group (JPEG) images compared to the previous methods.

scholarly journals An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

2019 ◽  
Vol 2019 ◽  
pp. 1-25
Author(s):  
Yanzhu Hu ◽  
Jiao Wang ◽  
Xinbo Ai ◽  
Xu Zhuang
Keyword(s):  
Cost Function ◽  
Artificial Bee Colony ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Segmentation Algorithm ◽  
Bee Colony ◽  
Segmentation Of Images ◽  
The Cost

In order to realize the multithreshold segmentation of images, an improved segmentation algorithm based on graph cut theory using artificial bee colony is proposed. A new weight function based on gray level and the location of pixels is constructed in this paper to calculate the probability that each pixel belongs to the same region. On this basis, a new cost function is reconstructed that can use both square and nonsquare images. Then the optimal threshold of the image is obtained through searching for the minimum value of the cost function using artificial bee colony algorithm. In this paper, public dataset for segmentation and widely used images were measured separately. Experimental results show that the algorithm proposed in this paper can achieve larger Information Entropy (IE), higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), smaller Root Mean Squared Error (RMSE), and shorter time than other image segmentation algorithms.

Denoising and 3D Reconstruction of CT Images in Extracted Tooth via Wavelet and Bilateral Filtering

2018 ◽  
Vol 32 (05) ◽  
pp. 1854010 ◽  
Author(s):  
Cuizhen Wang ◽  
Zhenxue Chen ◽  
Yan Wang ◽  
Zhifeng Wang
Keyword(s):  
3D Reconstruction ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Similarity Index ◽  
Low Frequency ◽  
Structural Similarity ◽  
Bilateral Filtering ◽  
Denoising Method ◽  
Dimensional Reconstruction ◽  
Index Measure

Three-dimensional reconstruction of teeth plays an important role in the operation of living dental implants. However, the tissue around teeth and the noise generated in the process of image acquisition bring a serious impact on the reconstruction results, which must be reduced or eliminated. Combined with the advantages of wavelet transform and bilateral filtering, this paper proposes an image denoising method based on the above methods. The method proposed in this paper not only removes the noise but also preserves the image edge details. The noise in high frequency subbands is denoised using a locally adaptive thresholding and the noise in low frequency subbands is filtered by the bilateral filtering. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and 3D reconstruction using the iso-surface extraction method are used to evaluate the denoising effect. The experimental results show that the proposed method is better than the wavelet denoising and bilateral filtering, and the reconstruction results meet the requirements of clinical diagnosis.

scholarly journals New model for local fractional integral of Chebyshev polynomials for image denoising

2019 ◽  
pp. 22-28
Author(s):  
Suzan J Obaiys ◽  
Hamid A Jalab ◽  
Rabha W Ibrahim
Keyword(s):  
Image Denoising ◽  
Chebyshev Polynomials ◽  
Fractional Integral ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Input Image ◽  
Convolution Product ◽  
Local Fractional Integral ◽  
Image Pixels

The use of local fractional calculus has increased in different applications of image processing. This study proposes a new algorithm for image denoising to remove Gaussian noise in digital images. The proposed algorithm is based on local fractional integral of Chebyshev polynomials. The proposed structures of the local fractional windows are obtained by four masks created for x and y directions. On four directions, a convolution product of the input image pixels with the local fractional mask window has been performed. The visual perception and peak signal-to-noise ratio (PSNR) with the structural similarity index (SSIM) are used as image quality measurements. The experiments proved that the accomplished filtering results are better than the Gaussian filter. Keywords: local fractional; Chebyshev polynomials; Image denoising

scholarly journals Multi-wavelet level comparison on compressive sensing for MRI image reconstruction

2020 ◽  
Vol 9 (4) ◽  
pp. 1461-1467
Author(s):  
Indrarini Dyah Irawati ◽  
Sugondo Hadiyoso ◽  
Yuli Sun Hariyani
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Gaussian Random Process ◽  
Structural Similarity Index ◽  
Level 3 ◽  
Mri Reconstruction ◽  
Multi Level ◽  
Index Measure ◽  
Level 2

In this study, we proposed compressive sampling for MRI reconstruction based on sparse representation using multi-wavelet transformation. Comparing the performance of wavelet decomposition level, which are Level 1, Level 2, Level 3, and Level 4. We used gaussian random process to generate measurement matrix. The algorithm used to reconstruct the image is . The experimental results showed that the use of wavelet multi-level can generate higher compression ratio but requires a longer processing time. MRI reconstruction results based on the parameters of the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) show that the higher the level of decomposition in wavelets, the value of both decreases.

Wavelet-Based Image Compression Encoding Techniques — A Complete Performance Analysis

2020 ◽  
Vol 20 (02) ◽  
pp. 2050008
Author(s):  
S. P. Raja
Keyword(s):  
Image Compression ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Absolute Error ◽  
Set Partitioning ◽  
Complete Analysis ◽  
Structural Content ◽  
Image Quality Index

This paper presents a complete analysis of wavelet-based image compression encoding techniques. The techniques involved in this paper are embedded zerotree wavelet (EZW), set partitioning in hierarchical trees (SPIHT), wavelet difference reduction (WDR), adaptively scanned wavelet difference reduction (ASWDR), set partitioned embedded block coder (SPECK), compression with reversible embedded wavelet (CREW) and spatial orientation tree wavelet (STW). Experiments are done by varying level of the decomposition, bits per pixel and compression ratio. The evaluation is done by taking parameters like peak signal to noise ratio (PSNR), mean square error (MSE), image quality index (IQI) and structural similarity index (SSIM), average difference (AD), normalized cross-correlation (NK), structural content (SC), maximum difference (MD), Laplacian mean squared error (LMSE) and normalized absolute error (NAE).

scholarly journals Digital Image Steganography Scheme Based on DWT and SVD

2020 ◽  
Vol 13 (4) ◽  
pp. 10-17
Author(s):  
Fadhil Kadhim Zaidan
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Image Steganography ◽  
Discrete Wavelet ◽  
Grayscale Image ◽  
Structural Similarity Index ◽  
Value Decomposition ◽  
Index Measure

In this work, a grayscale image steganography scheme is proposed using a discrete wavelet transform (DWT) and singular value decomposition (SVD). In this scheme, 2-level DWT is applied to a cover image to obtain the high frequency band HL2 which is utilized to embed a secret grayscale image based on the SVD technique. The robustness and the imperceptibility of the proposed steganography algorithm are controlled by a scaling factor for obtaining an acceptable trade-off between them. Peak signal to noise ratio (PSNR) and Structural Similarity Index Measure (SSIM) are used for assessing the efficiency of the proposed approach. Experimental results demonstrate that the proposed scheme still holds its validity under different known attacks such as noise addition, filtering, cropping and JPEG compression

scholarly journals Image Watermarking Based on IWT and Parity Bit Checking

2021 ◽  
pp. 2726-2739
Author(s):  
Jalal H. Awad ◽  
Balsam D. Majeed
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Floating Point ◽  
Integer Coefficient ◽  
Authentication Mechanism ◽  
Image Pixels

     Various document types play an influential role in a lot of our lives activities today; hence preserving their integrity is an important matter. Such documents have various forms, including texts, videos, sounds, and images.  The latter types' authentication will be our concern here in this paper. Images can be handled spatially by doing the proper modification directly on their pixel values or spectrally through conducting some adjustments to some of the addressed coefficients. Due to spectral (frequency) domain flexibility in handling data, the domain coefficients are utilized for the watermark embedding purpose. The integer wavelet transform (IWT), which is a wavelet transform based on the lifting scheme, is adopted in this paper in order to provide a direct way for converting image pixels' integer values to integer coefficient values rather than floating point coefficients that could be produced by the traditional wavelet transform. This direct relation can enhance the processed image quality due to avoiding the rounding operations on the floating point coefficients. The well-known parity bit approach is also utilized in this paper as an authentication mechanism, where 3 secret parity bits are used for each block in an image which is divided into non-overlapped blocks in order to enforce a form of fragile watermark approach. Thus, any alteration in the block pixels could cause the adopted (even) parity to be violated. The fragile watermarking is achieved through the modification of least significant bits ((LSBs) of certain frequency coefficients' according to the even parity condition. In spite of this image watermarking operation, the proposed method is efficient. In order to prove the efficiency of our proposed method, it was tested against standard images using measurements like peak signal to noise ratio (PSNR) and structural similarity index (SSIM).  Experiments showed promising results; the method preserves high image quality (PSNR≈ 44.4367dB, SSIM≈ 0.9956) and good tamper detection capability.

scholarly journals Research on the Image Denoising Method Based on Partial Differential Equations

2016 ◽  
Vol 16 (5) ◽  
pp. 109-118
Author(s):  
Xiaolu Xie
Keyword(s):  
Image Denoising ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Structural Similarity Index ◽  
Denoising Method ◽  
New Approach ◽  
Proposed Model ◽  
Index Measure

Abstract In this paper we propose a new approach for image denoising based on the combination of PM model, isotropic diffusion model, and TV model. To emphasize the superiority of the proposed model, we have used the Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) as the subjective criterion. Numerical experiments with different images show that our algorithm has the highest PSNR and SS1M, as well as the best visual quality among the six algorithms. Experimental results confirm the high performance of the proposed model compared with some well-known algorithms. In a word, the new model outperforms the mentioned three well known algorithms in reducing the Gibbs-type artifacts, edges blurring, and the block effect, simultaneously.

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