scholarly journals Backlight Unit Excessive Dimming Based on Perceptual Image Analysis

2021 ◽  
Vol 11 (3) ◽  
pp. 1039
Author(s):  
Seungwoo Yang ◽  
Hyungsik Shin ◽  
JaeHee You
Keyword(s):  
Power Consumption ◽  
Visual System ◽  
Human Visual System ◽  
Similarity Index ◽  
Structural Similarity ◽  
Opinion Score ◽  
The Mean ◽  
Mean Luminance ◽  
Index Measure ◽  
Contrast Response

A method to maximize backlight dimming of liquid crystal displays (LCDs) based on human visual system are proposed to minimize power consumption of display panels. Based on images, the proposed method optimizes global, local, and red, green, blue (RGB) backlight dimming by enhancing dimming about 12% on average for various images while maintaining tolerable degradation of perceived image qualities in pixel saturation areas. The method considers and utilizes the brightness sensitivity and contrast response functions of human visual system using the mean luminance and the contrast of an image, which are mathematically modelled to allow optimization for display panels and application areas. A simulator that can calculate various dimming cases with the evaluations of numerical and perceptual image qualities as well as power consumption amount is introduced. With pattern and real photo images, the degree of power savings and the preservation of image quality of the proposed method are verified to outperform conventional approaches with high scores of the mean opinion score (MOS) and the structural similarity index measure (SSIM) over 0.97 while saving more than 10% of power dissipation.

scholarly journals Tinjauan Literatur Sistematik tentang Structural Similarity Index Measure untuk Deteksi Anomali Gambar

2021 ◽  
Vol 7 (2) ◽  
pp. 75
Author(s):  
Halim Bayuaji Sumarna ◽  
Ema Utami ◽  
Anggit Dwi Hartanto
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Human Visual System ◽  
Vision System ◽  
Similarity Index ◽  
Structural Similarity ◽  
Human Vision ◽  
Human Vision System ◽  
Structural Similarity Index ◽  
Index Measure

Image enhancement merupakan prosedur yang digunakan untuk memproses gambar sehingga dapat memperbaiki atau meningkatkan kualitas gambar agar selanjutnya dapat dianalis untuk tujuan tertentu. Ada banyak algoritma image enhancement yang dapat diterapkan pada suatu gambar, salah satunya dapat menggunakan algoritma structural similarity index measure (SSIM), algoritma ini berfungsi sebagai alat ukur dalam menilai kualitas gambar, bekerja dengan membandingkan fitur structural dari gambar, dan kualitas gambar dijelaskan oleh kesamaan structural. Selain untuk menilai kualitas suatu gambar, SSIM dapat menjadi metode dalam menganalisis perbedaan gambar, sehingga diketahui anomali dari perbandingan dua gambar berdasarkan data structural dari sebuah gambar. Tinjauan literature sistematis ini digunakan untuk menganalisis dan fokus pada algoritma SSIM dalam mengetahui anomaly 2 gambar yang terlihat mirip secara human visual system. Hasil sistematis review menunjukkan bahwa penggunaan algoritma SSIM dalam menilai kualitas gambar berkorelasi kuat dengan HVS (Human Vision System) dan dalam deteksi anomaly gambar menghasilkan akurasi yang berbeda, karena terpengaruh intensitas cahaya dan posisi kamera dalam mengambil gambar sebagai dataset.Kata Kunci— SSIM, anomaly, gambar, deteksiImage enhancement is a procedure used to process images so that they can correct or improve image quality so that they can then be analyzed for specific purposes. Many image enhancement algorithms can be applied to an image. one of the usable methods is the structural similarity index measure (SSIM) algorithm, this algorithm serves as a measuring tool in assessing image quality. It works by comparing the structural features of images, and the image quality is explained by structural similarity. In addition to assessing the quality of an image, SSIM can be a method of analyzing image differences. So, the anomalies are known from the comparison of two images based on the structural data from an image. This systematic literature review is used to analyze and focus on the SSIM algorithm in knowing anomaly 2 images that look similar to the human visual system. Systematic review results show that the use of the SSIM algorithm in assessing image quality is strongly correlated with HVS (Human Vision System). In anomaly detection of images produces different accuracy because it is affected by light intensity and camera position in taking pictures as a dataset.Keywords— SSIM, anomaly, gambar, deteksi

Masking by Uniform Field Flicker: Some Practical Problems

Perception ◽  
1987 ◽  
Vol 16 (5) ◽  
pp. 641-647 ◽  
Author(s):  
David R Badcock ◽  
Elizabeth Sevdalis
Keyword(s):  
Visual System ◽  
Contrast Sensitivity ◽  
Human Visual System ◽  
Sensitivity Function ◽  
Relative Performance ◽  
Contrast Sensitivity Function ◽  
Uniform Field ◽  
Spatial Frequencies ◽  
The Mean ◽  
Mean Luminance

The technique of uniform field flicker (UFF) masking has frequently been used to address issues concerning the relative performance of sustained and transient neural channels in the human visual system. Unfortunately there has been an artifact in the implementation of this method in most published experiments which has meant that the contrast of the target has been flickered in synchrony with the mean luminance. A study is reported in which the artifact was corrected and the effects of UFF masking on the contrast sensitivity function then examined. With this correction, masking was still restricted to low spatial frequencies but it was much weaker than reported originally. It is argued that the original evidence suggesting that UFF masking can be used to examine the functioning of transient and sustained channels has not been interpreted correctly and that the basis for such a claim is weak.

scholarly journals Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Diego José Luis Botia Valderrama ◽  
Natalia Gaviria Gómez
Keyword(s):  
Neural Networks ◽  
Quality Of Experience ◽  
Video Quality ◽  
Similarity Index ◽  
Correlation Coefficients ◽  
Structural Similarity ◽  
Opinion Score ◽  
Measurement And Evaluation ◽  
Rate Correlation

The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video Quality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service) based in the strategyDiffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.

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 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.

scholarly journals Hyperspectral Image Restoration under Complex Multi-Band Noises

2018 ◽  
Vol 10 (10) ◽  
pp. 1631 ◽  
Author(s):  
Zongsheng Yue ◽  
Deyu Meng ◽  
Yongqing Sun ◽  
Qian Zhao
Keyword(s):  
Dirichlet Process ◽  
Hyperspectral Image ◽  
Similarity Index ◽  
Distinct Type ◽  
Structural Similarity ◽  
Gaussian Mixture ◽  
Noise Characteristics ◽  
Restoration Technique ◽  
The Mean ◽  
Bayes Algorithm

Hyperspectral images (HSIs) are always corrupted by complicated forms of noise during the acquisition process, such as Gaussian noise, impulse noise, stripes, deadlines and so on. Specifically, different bands of the practical HSIs generally contain different noises of evidently distinct type and extent. While current HSI restoration methods give less consideration to such band-noise-distinctness issues, this study elaborately constructs a new HSI restoration technique, aimed at more faithfully and comprehensively taking such noise characteristics into account. Particularly, through a two-level hierarchical Dirichlet process (HDP) to model the HSI noise structure, the noise of each band is depicted by a Dirichlet process Gaussian mixture model (DP-GMM), in which its complexity can be flexibly adapted in an automatic manner. Besides, the DP-GMM of each band comes from a higher level DP-GMM that relates the noise of different bands. The variational Bayes algorithm is also designed to solve this model, and closed-form updating equations for all involved parameters are deduced. The experiment indicates that, in terms of the mean peak signal-to-noise ratio (MPSNR), the proposed method is on average 1 dB higher compared with the existing state-of-the-art methods, as well as performing better in terms of the mean structural similarity index (MSSIM) and Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS).

scholarly journals Franken-CT: Head and Neck MR-Based Pseudo-CT Synthesis Using Diverse Anatomical Overlapping MR-CT Scans

2021 ◽  
Vol 11 (8) ◽  
pp. 3508
Author(s):  
Pedro Miguel Martinez-Girones ◽  
Javier Vera-Olmos ◽  
Mario Gil-Correa ◽  
Ana Ramos ◽  
Lina Garcia-Cañamaque ◽  
...  
Keyword(s):  
Deep Learning ◽  
Confidence Interval ◽  
Head And Neck ◽  
Similarity Index ◽  
Ground Truth ◽  
Structural Similarity ◽  
Absolute Error ◽  
Ct Scans ◽  
Learning Methods ◽  
Index Measure

Typically, pseudo-Computerized Tomography (CT) synthesis schemes proposed in the literature rely on complete atlases acquired with the same field of view (FOV) as the input volume. However, clinical CTs are usually acquired in a reduced FOV to decrease patient ionization. In this work, we present the Franken-CT approach, showing how the use of a non-parametric atlas composed of diverse anatomical overlapping Magnetic Resonance (MR)-CT scans and deep learning methods based on the U-net architecture enable synthesizing extended head and neck pseudo-CTs. Visual inspection of the results shows the high quality of the pseudo-CT and the robustness of the method, which is able to capture the details of the bone contours despite synthesizing the resulting image from knowledge obtained from images acquired with a completely different FOV. The experimental Zero-Normalized Cross-Correlation (ZNCC) reports 0.9367 ± 0.0138 (mean ± SD) and 95% confidence interval (0.9221, 0.9512); the experimental Mean Absolute Error (MAE) reports 73.9149 ± 9.2101 HU and 95% confidence interval (66.3383, 81.4915); the Structural Similarity Index Measure (SSIM) reports 0.9943 ± 0.0009 and 95% confidence interval (0.9935, 0.9951); and the experimental Dice coefficient for bone tissue reports 0.7051 ± 0.1126 and 95% confidence interval (0.6125, 0.7977). The voxel-by-voxel correlation plot shows an excellent correlation between pseudo-CT and ground-truth CT Hounsfield Units (m = 0.87; adjusted R2 = 0.91; p < 0.001). The Bland–Altman plot shows that the average of the differences is low (−38.6471 ± 199.6100; 95% CI (−429.8827, 352.5884)). This work serves as a proof of concept to demonstrate the great potential of deep learning methods for pseudo-CT synthesis and their great potential using real clinical datasets.

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.

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