Peak Signal-to Ratio Research Articles

Application of Multi-Level Thresholding Fuzzy Entropy and Differential Evolution Techniques to Medical Image Compression

Each storage medium possesses a capacity that is vital to technological progress in supporting internet speed for downloading extensive data. Effective storage media management includes compressing image data to optimize available space utilization for efficient usage. Digital processing of image data for each patient over a specific duration is crucial. This study proposes medical image compression utilizing multi-level thresholding employing the fuzzy entropy and differential evolution (FE-DE) algorithms. The compression results are subsequently assessed using the Peak Signal to Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM) to appraise the algorithm's performance and the quality of the compressed images. Employing Fuzzy Compression yields increasing PSNR, SSIM, and FSIM values as the threshold level rises, with the highest values achieved at threshold level 40. The increase in value is in line with the visual quality and compressed image produced.

Digital image steganography by using edge adaptive based chaos cryptography

Steganography and Cryptography is the method used to secure the information. Cryptography is used the principle of a Chaos by using Cat Arnold Map function to assure randomness. While in steganography, the method is used Least Significant Bit Matching Revisited (LSBMR). Embedding region was on edge digital imagery to ensure the message was not detected in the image by visual. The method used to detect the edge region by using Canny edge Detection. The test results obtained by Peak Signal to Ratio (PSNR) are 72-44 dB for data messages with a size of 10 % of the media cover.

Improved Image Denoising with the Integrated Model of Gaussian filter and Neighshrink SURE

Image denoising, being an important preprocessing stage in image processing, minimizes the noise interfering with the information content of the image. The denoising problems are addressed by various techniques starting from the Fourier transforms to wavelets. Because of the localized time frequency features and advantages of multi resolution capabilities, the wavelets have been extensively used in the denoising process. The development of algorithms for the wavelet thresholding or shrinkage strategies along with different filters have resulted in the betterment of image quality after the denoising. Even though the image denoising algorithm based on a combination of Gaussian and Bilateral filters, shows good performance but lacks in consistency with respect to the noise levels and also the type of images used. This paper discusses the advantages of NeighShrink SURE rule in developing an effective thresholding strategy, thereby proposing a improved denoising method incorporating the NeighShrink SURE rule along with combination of Gaussian filter model. The methodology employs the use of subband thresholding derived from the NeighShrink SURE rule. The outcome of the proposed method exhibits a comparatively improved performance in Peak Signal to Ratio (PSNR) and Image Quality Index (IQI) values of the test images.

A fast algorithm for YCbCr to perception color model conversion based on fixed-point DSP

Real-time video processing heavily relies on the color space conversion. Due to the real-time requirement, the traditional conversion methods often suffer from the moderate conversion speed, inaccuracy and low video quality. In order to meet the efficiency requirement of color space conversion in real-time video systems, we present a fast algorithm for color space YCbCr to perception color model conversion, which is based on the simplified shift and look-up table (SSLUT). The approach can be divided into two steps. At first, the simplified fixed-point shift method is used to convert YCbCr to RGB. Then look-up table assists with the YCbCr to perception color model conversion, such as HSV, HSI and HSL. To validate the proposed fast algorithm, the conversion speed and accuracy are compared with the traditional methods based on Code Composer Studio (CCS) test platform. Moreover, we make the video quality evaluation by using the peak signal-to-ratio (PSNR) and the structural similarity (SSIM). Experimental results illustrate the real-time, robustness and accuracy of the fast algorithm.

Mixed Higher Order Variational Model for Image Recovery

A novel mixed higher order regularizer involving the first and second degree image derivatives is proposed in this paper. Using spectral decomposition, we reformulate the new regularizer as a weightedL1-L2mixed norm of image derivatives. Due to the equivalent formulation of the proposed regularizer, an efficient fast projected gradient algorithm combined with monotone fast iterative shrinkage thresholding, called, FPG-MFISTA, is designed to solve the resulting variational image recovery problems under majorization-minimization framework. Finally, we demonstrate the effectiveness of the proposed regularization scheme by the experimental comparisons with total variation (TV) scheme, nonlocal TV scheme, and current second degree methods. Specifically, the proposed approach achieves better results than related state-of-the-art methods in terms of peak signal to ratio (PSNR) and restoration quality.

COMPARISON OF DENOISING METHODS FOR DIGITAL MAMMOGRAPHIC IMAGE

We compared effects of denoising methods on digital mammographic images. The denoising methods studied were an adaptive Wiener filter and low–pass Gaussian filter. The denoising methods were applied as an image preprocessing techniques before enhancement. The performance of image denoising methods are based on Mean Squared Error (MSE) and Peak Signal To Ratio (PSNR) values.

De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering

Hyperspectral imagers are built line-by-line similar to images acquired by pushbroom sensors. They can experience striping artifacts due to variations in detector response to incident imagery. In this research, a method for hyperspectral image de-striping based on wavelet analysis and adaptive Fourier zero-frequency amplitude normalization has been developed. The algorithm was tested against three other de-striping algorithms. Hyperspectral image bands of different scenes with significant striping and random noise, as well as an image with simulated noise, were used in the testing. The results were assessed visually and quantitatively using frequency domain Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE) and/or Peak Signal-to-Ratio (PSNR). The results demonstrated the superiority of our proposed algorithm in de-striping hyperspectral images without introducing unwanted artifacts, yet preserving image details. In the noise-induced image results, the proposed method reduced RMSE error and improved PSNR by 3.5 dB which is better than other tested methods. A Combined method, integrating the proposed algorithm with a generic wavelet-based de-noising algorithm, showed significant random noise suppression in addition to stripe reduction with a PSNR value of 4.3 dB. These findings make the algorithm a candidate for practical implementation on remote sensing images including high resolution hyperspectral images contaminated with stripe and random noise.

An improved frame-layer bit allocation scheme for H.264/AVC rate control

In this paper, we aim at improving the video quality degradation due to high motions or scene changes. An improved frame-layer bit allocation scheme for H.264/AVC rate control is proposed. First, current frame is pre-encoded in 16×16 modes with a fixed quantization parameter (QP). The frame coding complexity is then measured based on the resulting bits and peak signal-to-ratio (PSNR) in the pre-coding stage. Finally, a bit budget is calculated for current frame according to its coding complexity and inter-frame PSNR fluctuation, combined with the buffer status. Simulation results show that, in comparison with the H.264 adopted rate control scheme, our method is more efficient to suppress the sharp PSNR drops caused by high motions and scene changes. The visual quality variations in a sequence are also relieved.

H.264/AVC baseline profile to MPEG‐4 visual simple profile transcoding to reduce the spatial resolution

AbstractIn this study, we propose a heterogeneous transcoding method of converting an H.264/Advanced video coding (AVC) Baseline profile (BP) video bitstream into an MPEG‐4 Visual simple profile (VSP) video bitstream. The proposed method reduces the spatial resolution for mobile terminals, which support only low resolution video bitstreams. When the H.264/AVC BP video bitstream is transformed into the MPEG‐4 VSP video bitstream, the conversions between the H.264/AVC BP block types and the MPEG‐4 VSP block types are performed by analyzing the macroblocks (MBs) conversion probability and calculating the difference values of motion vector. The proposed transcoding method runs on average 5.5 times faster than the cascaded transcoding methods, for a degradation of the PSNR (peak‐signal‐to ratio) of less than 0.5 dB. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 24–33, 2006