Average Peak Signal-to-noise Ratio Research Articles

An Automated Security Approach of Video Steganography–Based LSB Using FPGA Implementation

Steganography has become one of the most significant techniques to conceal secret data in media files. This paper proposes a novel automated methodology of achieving two levels of security for videos, which comprise encryption and steganography techniques. The methodology enhances the security level of secret data without affecting the accuracy and capacity of the videos. In the first level, the secret data is encrypted based on Advanced Encryption Standard (AES) algorithm using Java language, which renders the data unreadable. In the second level, the encrypted data is concealed in the video frames (images) using FPGA hardware implementation that renders the data invisible. The steganographic technique used in this work is the least significant bit (LSB) method; a 1–1–0 LSB scheme is used to maintain significantly high frame imperceptibility. The video frames used as cover files are selected randomly by the randomization scheme developed in this work. The randomization method scatters the data throughout the video frames rendering the retrieval of the data in its original order, without a proper key, a challenging task. The experimental results of concealment of secret data in video frames are presented in this paper and compared with those of similar approaches. The performance in terms of area, power dissipation, and peak signal-to-noise ratio (PSNR) of the proposed method outperformed traditional approaches. Furthermore, it is demonstrated that the proposed method is capable of automatically embedding and extracting the secret data at two levels of security on video frames, with a 57.1[Formula: see text]dB average PSNR.

Choroidal neovascularization imaging using multiple en face optical coherence tomography angiography image averaging.

To determine the effects of averaging five en face optical coherence tomography angiographic (OCTA) images on the quality of the images in eyes with a choroidal neovascularization (CNV). Twenty-seven eyes of 25 patients (18 men, 7 women; average age 71.0years) with a CNV were examined by OCTA (OCT HS-100, Canon. Japan). A 3 × 3-mm image including the CNV was recorded and automatically segmented between the retinal outer layers. Analyses were performed on a single image (S-image) and the average of five single images of the same area (A-images). The region of the CNV was selected by ImageJ, and the peak signal-to-noise ratio (PSNR), the vascular density (VD), fractal dimension (FD), and the noise component using band pass filter (BPF) processing of the S- and A-images of each case were compared. The average PSNR for the A-images was 14.0 which was significantly higher than the 12.2 for the S-images (P < 0.01). However, the average VD was 33.6% for the S-images and 34.8% for the A-images (P > 0.1). The average FD was 1.67 for the S-images and 1.54 for the A-images (P < 0.01). The mean luminance difference obtained by subtracting the luminance of the A-image from the S-image after BPF processing was 10.41 ± 14.66db which was positive for all eyes. The better quality of the A-images of a CNV and absence of a significant difference in the vascular density indicates that the improvement was due to the removal of the same signal levels of the noise component and blood vessels.

Comparative Performance of Filtering Methods for Reducing Noise in Ischemic Posterior Fossa CT Images

Posterior Fossa (PF) of head Computed Tomography (CT) image appears to be one of the intricate regions as it is located in the deepest part of the brain. This appears to cause significant degradation in CT image quality due to the effect of beam hardening and bone thickness. Whilst these issues may not fundamentally limit the scanning procedure, it does appear to be the contributory factors in reducing the performance of the ischemic diagnosis procedure. Thus, it is seen that image filtering is playing an important role in improving the CT image quality and effectively eliminates the influence of Poisson noise in the PF region. Therefore, this paper attempts to assess the feasibility of four different filtering methods; Anisotropic diffusion, Bilateral, Median and Wiener to eliminate noise in the CT image of PF containing ischemic. To the best of our knowledge, this is the first study to report the performance of filtering in ischemic PF. The efficacy of these four filters is evaluated in details using qualitative and quantitative metrics such as Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), Structural Similarity Index (SSIM) and processing time. The experimental works demonstrate that Bilateral filtering offers promising results in which this method can eliminate Poisson noise in CT images for ischemic PF with average PSNR, RMSE, SSIM values of 32.95, 5.7416 and 0.9749 respectively. This filter has provided the flexibility of being applicable even in cases where ischemic is presented in PF.

Resolution-Preserving Generative Adversarial Networks for Image Enhancement

Generative adversarial networks (GANs) are used for image enhancement such as single image super-resolution (SISR) and deblurring. The conventional GANs-based image enhancement suffers from two drawbacks that cause a quality degradation due to a loss of detailed information. First, the conventional discriminator network adopts strided convolution layers which cause a reduction in the resolution of the feature map, and thereby resulting in a loss of detailed information. Second, the previous GANs for image enhancement use the feature map of the visual geometry group (VGG) network for generating a content loss, which also causes visual artifacts because the maxpooling layers in the VGG network result in a loss of detailed information. To overcome these two drawbacks, this paper presents a proposal of a new resolution-preserving discriminator network architecture which removes the strided convolution layers, and a new content loss generated from the VGG network without maxpooling layers. The proposed discriminator network is applied to the super-resolution generative adversarial network (SRGAN), which is called a resolution-preserving SRGAN (RPSRGAN). Experimental results show that RPSRGAN generates more realistic super-resolution images than SRGAN does, and consequently, RPSRGAN with the new content loss improves the average peak signal-to-noise ratio (PSNR) by 0.75 dB and 0.32 dB for super-resolution images with the scale factors of 2 and 4, respectively. For deblurring, the visual appearance is also significantly improved, and the average PSNR is increased by 1.54 dB when the proposed discriminator and content loss are applied to the deblurring adversarial network.

A constrained total variation model for single image dehazing

Haze removal (or dehazing) is very important for many applications in computer vision. Because depth information and atmospheric light are usually unknown in practice, haze removal is a challenging problem, especially for single image dehazing. In this paper, we propose a new variational model for removing haze from a single input image. The proposed model combines Koschmieder’s law with Retinex assumption that an image is the product of illumination and reflection. We assume that scene depth and surface radiance are spatially piecewise smooth, total variation is thus used for regularization in our model. The proposed model is defined as a constrained optimization problem, which is solved by an alternating minimization scheme and a fast gradient projection algorithm. Theoretical analyses are given for the proposed model and algorithm. Some numerical examples are presented, which have shown that our model has the best visual effect and the highest average PSNR (Peak Signal-to-Noise Ratio) compared to six relevant models in the literature.

QoE-Driven Resource Allocation for Live Video Streaming Over D2D-Underlaid 5G Cellular Networks

Recently, device-to-device (D2D)-underlaid fifth-generation (5G) cellular networks have received plenty of attention because of their ability to save network resources and to reduce energy consumption. Most existing algorithms for multimedia services over D2D networks consider only the signal-to-noise ratio (SNR) and ignore temporal requirements, which do not provide optimum performances. To overcome this issue, we propose a framework for a cross-layer D2D link control system, which guarantees the quality of service and improves the quality of experience (QoE) for live video streaming with different priorities and delay constraints. In this framework, we considered three techniques, including priority-based video transmission, flexible communication mode switching of user equipment, and subset-based relay assignment. According to the live video generation period, our system dynamically adjusts the ratio between cellular and D2D mode durations in each unit of the communication period for each user individually. Our proposal also considerably reduces the duration and frequency of video playback freezing by delivering at least the minimum service quality of the delivered video to the sink users even in the shadow area and therefore improves the QoE for all users while minimizing energy consumption. System-level simulation shows that the proposed algorithm outperforms other methods in terms of the average mean time to failure, average peak SNR, and average energy consumption.

Average PSNR Optimized Cross Layer User Grouping and Resource Allocation for Uplink MU-MIMO OFDMA Video Communications

Wang et al. proposed cross layer resource allocation according to the channel state information of the physical layer and the video rate distortion of the application layer for uplink orthogonal frequency division multiple access (OFDMA) video transmission systems. Its object function is minimizing sum of all users’ video distortion. It shows significant improvement in average peak-signal-to-noise-ratio (PSNR) over the schemes which consider either the physical layer or application layer. However, minimizing sum of all users’ video distortion does not necessarily maximize average of all users’ PSNR. In this paper, we change the objective function for optimization to maximize average PSNR, and derive new optimal condition for cross layer subcarrier assignment for infinitesimal bandwidth increment. Furthermore, we extend to a multi-user multiple-input and multiple-output (MU-MIMO) uplink transmission system to improve information rate and thus video quality. We then propose an average PSNR optimized physical/application cross-layer user grouping and resource allocation for uplink MU-MIMO OFDMA video communications. The simulation results show that the proposed scheme has 4 dB PSNR gain over the previous Wang et al. cross-layer scheme and 5 dB PSNR gain over the previous physical layer only scheme for four users and SNR 12–18 dB.

Demosaicing enhancement using pixel-level fusion

Bayer pattern has been widely used in commercial digital cameras. In NASA’s mast camera (Mastcams) onboard the Mars rover Curiosity, Bayer pattern has also been used in capturing the RGB bands. It is well known that debayering, also known as demosaicing in the literature, introduces artifacts such as false colors and zipper edges. In this paper, we first present four fusion approaches, including weighted and the well-known alpha-trimmed mean filtering approaches. Each fusion approach combines demosaicing results from seven debayering algorithms in the literature, which are selected based on their performance mentioned in other survey papers and the availability of open source codes. Second, we present debayering results using two benchmark image data sets: IMAX and Kodak. It was observed that none of the seven algorithms in the literature can yield the best performance in terms of peak signal-to-noise ratio (PSNR), CIELAB score, and subjective evaluation. Although the fusion algorithms are simple, it turns out that the debayering performance can be improved quite dramatically after fusion based on our extensive evaluations. In particular, the average PSNR improvements of the weighted fusion algorithm over the best individual method are 1.1 dB for the IMAX database and 1.8 dB for the Kodak database, respectively. Third, we applied the various algorithms to 36 actual Mastcam images. Subjective evaluation indicates that the fusion algorithms still work well, but not as good as the existing debayering algorithm used by NASA.

A Hybrid Tactic Model Intended for Video Compression Using Global Affine Motion and Local Free-Form Transformation Parameters

Video compression marks its necessity when a huge sized video needs to be transmitted. The process starts with the identification of GoP (group of pictures), which depends on I- (intra), B- (bidirectional) and P- (predicted) frames determination. GoP is fixed, where consecutive frames are placed in an orderly manner based on the GoP size. Conventionally, B-frames lead to buffering of memory within the past and future frames consuming more computational time. Such issues are handled by an adaptive framework for determining frames based on matching criteria rather than fixed GoP. NSEW (North–South–East–West) affine translation (NAT) is proposed for replacing B with either I- or P-frame. The proposed framework involves video compression using affine motion-based free-form transformation and video decompression using warping methodologies for the purpose of compressing and decompressing the video sequence, based on the resulted I- and P-frames. B-spline transformation was also initiated at local level along with global affine transformation to improve the subjective quality of the decompressed video sequence. The methodology was investigated for the file size, computational time, peak-signal-to-noise ratio (PSNR) and Structural Similarity index (SSIM), which proved the superiority of the proposed technique. Further, the methodology was also investigated with optimizing the affine motion parameters (AMP) using nonlinear least squares, Broyden–Fletcher–Goldfarb–Shanno (BFGS) and limited-memory BFGS which yet again proved to be far more superior to conventional techniques with an average PSNR of 38.98 dB with LBFGS. To further improve the subjective quality, affine B-spline-based motion estimation using LBFGS was implemented and observed the average PSNR gain to be 42.03 dB.

Watermarking based image authentication and tamper detection algorithm using vector quantization approach

In the present work, a novel image watermarking algorithm using vector quantization (VQ) approach is presented for digital image authentication. Watermarks are embedded in two successive stages for image integrity verification and authentication. In the first stage, a key based approach is used to embed robust zero level watermark using properties of indices of vector quantized image. In the second stage, semifragile watermark is embedded by using modified index key based (MIKB) method. Random keys are used to improve the integrity and security of the designed system. Further, to classify an attack quantitatively as acceptable or as a malicious attack, pixel neighbourhood clustering approach is introduced. Proposed approach is evaluated on 250 standard test images using performance measures such as peak signal to noise ratio (PSNR) and normalized hamming similarity (NHS). The experimental results shows that propose approach achieve average false positive rate 0.00024 and the average false negative rate 0.0012. Further, the average PSNR and tamper detection/localization accuracy of watermarked image is 42dB and 99.8% respectively; while tamper localization sensitivity is very high. The proposed model is found to be robust to common content preserving attacks while fragile to content altering attacks.

Performance enhanced spatial video compression using global affine frame reconstruction

The modern era of information technology suffers a serious loss due to the lack of a cutting edge methodology for storing mega sized videos. It is at this juncture, video compression makes a mark for its necessity. There have been several research outcomes where almost all researchers have followed a particular methodology of adopting GoP (Group of pictures) for video compression, focusing on I (Intra), B (Bi-directional) & P (Predicted) frame determination. These frames remain fixed throughout the process of GoP regardless of the camera motion. Moreover, it also leads to buffering of memory within the past and future thereby consuming more computational time for B-frames. These vital issues are handled by an adaptive framework of determining frames based on a matching criteria rather than utilizing fixed GoP pattern. NSEW affine translation (NAT) is introduced for replacing B-frames with either I or P frame. The framework involves VCAME (Video Compression using Affine Motion Estimation) & VDAW (Video Decompression using Affine Warping) methodologies for compressing and decompressing a video sequence, based on the resulted I & P frames. The methodology was investigated over four vital parameters, the file size, computational time, SSIM (Structural Similarity Index) & PSNR (Peak Signal to Noise ratio), which proved the superiority of the proposed technique. Further, the methodology was also investigated with optimizing the affine motion parameters (AMP) using nonlinear least squares, BFGS (Broyden–Fletcher–Goldfarb–Shanno) and Limited-memory BFGS which yet again proved to be far more superior than conventional techniques yielding an average PSNR gain of 2.52dB.

Transfer orthogonal sparsifying transform learning for phase retrieval

The phase retrieval (PR) problem of recovering an image from its Fourier magnitudes is an important issue. Several PR algorithms have been proposed to address this problem. Recent efforts of exploiting sparsity were developed to improve the performance of PR algorithms, such as the reconstruction quality, robustness to noise, and convergence behavior. In this paper, we propose a novel sparsity-based algorithm, which can adaptively learn an orthogonal sparsifying transform, and reconstruct the image simultaneously from the Fourier magnitudes. However, the estimated images at the early iterations are extremely bad. Training samples from these images cannot provide much useful information for sparsifying transform learning. To avoid unnecessary updating, an orthogonal sparsifying transform learning method based on transfer learning is proposed. Through transfer learning, we transfer the fixed sparsifying transform to an adaptive one. We apply this new sparsifying transform learning method to PR, and exploit the alternating directions method of multipliers (ADMM) technique to solve the formulated problem. Since the learnt sparsifying transform is adaptive to data, it favors better sparsity. Using this learnt sparsifying transform for image reconstruction can improve the reconstruction quality at low oversampling ratios. Experimental results show that the proposed PR algorithm can improve nearly 6 dB compared with the recently proposed PR-TIHP-l1 algorithm in terms of the average PSNR (Peak Signal to Noise Ratio) at oversampling ratio 2.47, 2.53, 2.59. Moreover, our algorithm is robust to noise and has better convergence behavior heuristically.

MRI reconstruction based on three regularizations: Total variation and two wavelets

Abstract This paper proposes an efficient algorithm for magnetic resonance imaging (MRI) reconstruction based on compressed sensing (CS) to further improve the accuracy of the reconstructed image and simultaneously take into account the reconstruction speed. The proposed algorithm makes full use of prior knowledge of the sparsity of the MR image in different transform domains. It uses the total variation (TV), the orthogonal Haar wavelet, and the orthogonal Daubechies20 wavelet as regularizations, on top the split Bregman iteration algorithm. Experimental results show that when the sample ratio is 20%, SNR is 30 dB, the average reconstruction PSNR of the proposed algorithm is 41.09 dB. The proposed method is compared to nonlinear conjugate gradients combined with the backtracking line-search method, split Bregman algorithm based on TV regularization method and split Bregman algorithm based on TV + orthogonal Haar wavelet regularizations method, and the average reconstruction peak signal to noise ratio (PSNR) increases 3.71 dB, 3.44 dB and 1.14 dB, respectively.

Adaptive Resource Allocation for Scalable Video Transmission over OFDMA Systems

We propose an adaptive resource allocation algorithm for scalable H.264 video transmission over orthogonal frequency-division multiple-access (OFDMA) systems. The algorithm explores the scalability of the video codec and the multiuser diversity of OFDMA systems to achieve enhanced efficiency and video quality. We use the peak signal-to-noise ratio (PSNR) as the measure metric of video quality, and jointly optimize the video layer extraction and radio resource allocation, considering the heterogeneity of video sources and channel conditions. Given the fact that different quality layers of the video have different importance, an adaptive modulation and coding scheme is proposed where a fixed coding rate is used for each quality layer, and unequal error protection is implemented for different layers. We also propose a subcarrier allocation algorithm to assign subcarriers for different layers of different users' videos to maximize the average PSNR of users while satisfying the base layer requirement. The proposed resource allocation algorithm has a low complexity and is suitable for practical implementations. We further develop an end-to-end simulation testbed to demonstrate the effectiveness of the proposed transmission scheme.

Hierarchical modulation for client-driven selective streaming of multi-view video over AWGN channels

We consider a client-driven selective streaming system for multi-view video, which is supported by scalable multi-view video coding (SMVC). The system is used to reduce bit-rates for efficient transmission of multi-view video. The transmit source is partitioned into three layers: a base and two enhancement layers. The base layer contains all views encoded by MVC at a low-quality, while each enhancement layer contains high-quality part of the selected left and right views, respectively. The base layer is more important than the enhancement layers to create 3D perception, albeit of low-quality, and should be more protected than the enhancement layers. In this paper, two-level and three-level hierarchical 64QAM are used to provide unequal error protection (UEP) for the client-driven selective streaming system. First we find the suitable hierarchical values, which is the ratio of the distances in hierarchical 64QAM, for the client-driven selective streaming system. Furthermore, we analyze numerically how the hierarchical 64QAM impacts peak signal-to-noise ratio (PSNR) performance of fast and slow-motion sequences. Simulation results show that the three-level hierarchical 64QAM outperforms both conventional 64QAM with Gray mapping and the two-level hierarchical 64QAM in terms of achieving the target average PSNR performance at a low SNR environment.

Adaptive low‐complexity motion estimation algorithm for high efficiency video coding encoder

High quality videos became an essential requirement in recent applications. High efficiency video coding (HEVC) standard provides an efficient solution for high quality videos at lower bit rates. On the other hand, HEVC comes with much higher computational cost. In particular, motion estimation (ME) in HEVC, consumes the largest amount of computations. Therefore, fast ME algorithms and hardware accelerators are proposed in order to speed-up integer ME in HEVC. This study presents a fast centre search algorithm (FCSA) and an adaptive search window algorithm (ASWA) for integer pixel ME in HEVC. In addition, centre adaptive search algorithm, a combination of the two proposed algorithms FCSA and ASWA, is proposed in order to achieve the best performance. Experimental results show notable speed-up in terms of encoding time and bit rate saving with tolerable peak signal-to-noise ratio (PSNR) quality degradation. The proposed fast search algorithms reduce the computational complexity of the HEVC encoder by 57%. This improvement is accompanied with a modest average PSNR loss of 0.014 dB and an increase by 0.6385% in terms of bit rate when compared with related works.

Improving the fingerprint verification performance of set partitioning coders at low bit rates

Wavelet transform combined with the set partitioning coders (SPC) are the most widely used fingerprint image compression approach. Many different SPC coders have been proposed in the literature to encode the wavelet transform coefficients a common feature of which is trying to maximize the global peak-signal-to-noise ratio (PSNR) at a given bit rate. Unfortunately, they have not considered the local variations of SNR within the compressed fingerprint image; therefore, different regions in the compressed image will have different ridge-valley qualities. This problem causes the verification performance to be decreased because minutiae and other useful features cannot be extracted precisely from the low-bit-rate-compressed fingerprint images. Contrast variation within the original image worsens the problem. This paper deals with those applications of fingerprint image compression in which high compression ratios and preserving or improving the verification performance of the compressed images are the main concern. We propose a compression scheme in which the local-SNR (signal-to-noise ratio) variations within the compressed image are minimized (and thus, general quality is maximized everywhere) by means of an iterative procedure. The proposed procedure can be utilized in conjunction with any SPC coder without the need to modify the SPC coder's algorithm. In addition, we used image enhancement to further improve the ridge-valley quality as well as the verification performance of the compressed fingerprint images through alleviating the leakage effect. We evaluated the compression and verification performances of some conventional and modern SPC coders including STW, EZW, SPIHT, WDR, and ASWDR combined with the proposed scheme. This evaluation was performed on the FVC2004 dataset with respect to measures including average PSNR curve versus bit rate, verification accuracy, detection error trade-off (DET) curve, and correlation of matching scores versus the average quality of involved fingerprint images. Simulation results showed considerable improvement on verification performance of all examined SPC coders, especially the SPIHT coder, by using the proposed scheme.

Image denoising algorithm based on the convolution of fractional Tsallis entropy with the Riesz fractional derivative

Image denoising is an important component of image processing. The interest in the use of Riesz fractional order derivative has been rapidly growing for image processing recently. This paper mainly introduces the concept of fractional calculus and proposes a new mathematical model in using the convolution of fractional Tsallis entropy with the Riesz fractional derivative for image denoising. The structures of n × n fractional mask windows in the x and y directions of this algorithm are constructed. The image denoising performance is assessed using the visual perception, and the objective image quality metrics, such as peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The proposed algorithm achieved average PSNR of 28.92 dB and SSIM of 0.8041. The experimental results prove that the improvements achieved are compatible with other standard image smoothing filters (Gaussian, Kuan, and Homomorphic Wiener).

Knowledge-based genetic algorithm approach to quantization table generation for the JPEG baseline algorithm

JPEG has played an important role in the image compressioneld for the past two decades. Quantization tables in the JPEG scheme is a key factor that is responsible for compression/qual trade-off. Finding the optimal quantization table is an open research problem. Studies recommend the genetic algorithm to generate the optimal solution. Recent reports revealed optimal quantization table generation based on a classical genetic algorithm (CGA). Although the CGA produces better results, it shows inefficiency in terms of convergence speed and productivity of feasible solutions. This paper proposes a knowledge-based genetic algorithm (KBGA), which combines the image characteristics and knowledge about image compressibility with CGA operators such as initialization, selection, crossover, and mutation for searching for the optimal quantization table. The experimental results show that the optimal quantization table generated using the proposed KBGA outperforms the default JPEG quantization table in terms of mean square error (MSE) and peak signal-to-noise ratio (PSNR) for target bits per pixel. The KBGA was also tested on a variety of images in three different bits values per pixel to show its strength. The proposed KBGA produces an average PSNR gain of 3.3% and average MSE gain of 20.6% over the default JPEG quantization table. The performance measures such as average untness value, likelihood of evolution leap, and likelihood of optimality are used to validate the efficacy of the proposed KBGA. The novelty of the KBGA lies in the number of generations used to attain an optimal solution as compared to the CGA. The validation results show that this proposed KBGA guarantees feasible solutions with better quality at faster convergence rates.

Video Error Correction Using Soft-Output and Hard-Output Maximum Likelihood Decoding Applied to an H.264 Baseline Profile

Error concealment has long been identified as the last line of defense against transmission errors. Since error handling is outside the scope of video coding standards, decoders may choose to simply ignore corrupted packets or attempt to decode their content. In this paper, we present a novel joint source-channel decoding approach that can be applied to received video packets containing transmission errors. Soft-output information is combined with our novel syntax-element-level maximum likelihood decoding framework to effectively extract valid macroblocks from corrupted H.264 slices. Simulation results show that our video error correction strategy provides an average peak signal-to-noise ratio (PSNR) improvement near 2 dB compared to the error concealment approach used by the H.264 reference software, as well as an average PSNR improvement of 0.8 dB compared to state-of-the-art error concealment. The proposed method is also applicable when only hard-information is available, in which case it performs better than state-of-the-art error concealment especially in high error conditions. Finally, in our simulations, the proposed method increased the decoder computational complexity by only 5% to 20%, making it applicable for real-time applications.