ResGAN: A Low-Level Image Processing Network to Restore Original Quality of JPEG Compressed Images

2019 ◽  
Author(s):  
Chunbiao Zhu ◽  
Yuanqi Chen ◽  
Yiwei Zhang ◽  
Shan Liu ◽  
Ge Li
Keyword(s):  
Image Processing ◽  
Low Level ◽  
Compressed Images ◽  
Processing Network

scholarly journals Reduction of Artefacts in JPEG-XR Compressed Images

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1214 ◽  
Author(s):  
Kai-Lung Hua ◽  
Ho Trang ◽  
Kathiravan Srinivasan ◽  
Yung-Yao Chen ◽  
Chun-Hao Chen ◽  
...  
Keyword(s):  
Image Processing ◽  
Document Image ◽  
File Size ◽  
Quantization Parameter ◽  
High Quality ◽  
Compressed Images ◽  
Extra Step ◽  
Image Displays ◽  
Jpeg Xr

The JPEG-XR encoding process utilizes two types of transform operations: Photo Overlap Transform (POT) and Photo Core Transform (PCT). Using the Device Porting Kit (DPK) provided by Microsoft, we performed encoding and decoding processes on JPEG XR images. It was discovered that when the quantization parameter is >1-lossy compression conditions, the resulting image displays chequerboard block artefacts, border artefacts and corner artefacts. These artefacts are due to the nonlinearity of transforms used by JPEG-XR. Typically, it is not so visible; however, it can cause problems while copying and scanning applications, as it shows nonlinear transforms when the source and the target of the image have different configurations. Hence, it is important for document image processing pipelines to take such artefacts into account. Additionally, these artefacts are most problematic for high-quality settings and appear more visible at high compression ratios. In this paper, we analyse the cause of the above artefacts. It was found that the main problem lies in the step of POT and quantization. To solve this problem, the use of a “uniform matrix” is proposed. After POT (encoding) and before inverse POT (decoding), an extra step is added to multiply this uniform matrix. Results suggest that it is an easy and effective way to decrease chequerboard, border and corner artefacts, thereby improving the image quality of lossy encoding JPEG XR than the original DPK program with no increased calculation complexity or file size.

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

scholarly journals Clay-Based Brick Porosity Estimation Using Image Processing Techniques

2019 ◽  
Vol 29 (1) ◽  
pp. 1226-1234
Author(s):  
Safa Jida ◽  
Hassan Ouallal ◽  
Brahim Aksasse ◽  
Mohammed Ouanan ◽  
Mohamed El Amraoui ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Computer Vision ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Porosity Estimation ◽  
Microscopy Images ◽  
Scanning Electron

Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.

scholarly journals Physical Activity as a Predictor of the Level of Stress and Quality of Sleep during COVID-19 Lockdown

2021 ◽  
Vol 18 (11) ◽  
pp. 5811
Author(s):  
Anna Lipert ◽  
Remigiusz Kozłowski ◽  
Dariusz Timler ◽  
Michał Marczak ◽  
Kamila Musiał ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Online Survey ◽  
Short Form ◽  
Well Being ◽  
Poor Quality ◽  
Quality Of Sleep ◽  
Low Level ◽  
The Government

Background: The coronavirus pandemic and the government restrictions significantly disturbed the daily functioning of people, thereby influencing healthy behaviors, such as physical activity—the core indicator of well-being. This study evaluates the associations between physical activity (PA), the level of stress and quality of sleep during the COVID-19 pandemic lockdown. Methods: An online survey was distributed during the governmental lockdown in April 2020 and included measures for assessing physical activity, stress and sleep. The surveyed participants included all adults aged 18 years and over. The final data were collected from the 1959 respondents using: International Physical Activity Questionnaire-Short Form (IPAQ-SF), Perceived Stress Scale (PSS) and Pittsburgh Sleep Quality Index (PSQI). Findings: Almost half of the respondents indicated a low level of PA, performing only 60 min of PA daily. Most of the participants reported a moderate or high level of stress (57% and 29%, respectively) and 64% of them reported poor quality of sleep. People with low levels of stress performed on average 85.1 min/day of walking (WPA), 40.9 min/day of moderate PA (MPA) or 52.6 min/day of vigorous PA (VPA). People with good quality of sleep performed 82.9 min/day of WPA, 43.6 min/day MPA and 40.5 min/day VPA. Interpretation: The results from the study indicate that the volume of daily PA may be a predictor of the level of stress and sleep quality in adults during the COVID-19 pandemic lockdown. To retain a low level of stress and good quality of sleep, a lifestyle that allows to achieve a moderate level of physical activity should be maintained. The optimal daily dose of PA is at least 70 min per day, involving different intensities.

Computing the Medical Image Registration Using Meta-Heuristics

2014 ◽  
Vol 643 ◽  
pp. 237-242 ◽  
Author(s):  
Tahari Abdou El Karim ◽  
Bendakmousse Abdeslam ◽  
Ait Aoudia Samy
Keyword(s):  
Image Processing ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Image Registration ◽  
Cost Function ◽  
Population Genetic ◽  
Medical Image ◽  
Medical Image Registration ◽  
Anatomical Structures

The image registration is a very important task in image processing. In the field of medical imaging, it is used to compare the anatomical structures of two or more images taken at different time to track for example the evolution of a disease. Intensity-based techniques are widely used in the multi-modal registration. To have the best registration, a cost function expressing the similarity between these images is maximized. The registration problem is reduced to the optimization of a cost function. We propose to use neighborhood meta-heuristics (tabu search, simulated annealing) and a meta-heuristic population (genetic algorithms). An evaluation step is necessary to estimate the quality of registration obtained. In this paper we present some results of medical image registration

Intermediate and low-level image processing with M3

1997 ◽  
Author(s):  
M. Fikret Ercan ◽  
Yu-Fai Fung ◽  
M. Suleyman Demokan
Keyword(s):  
Image Processing ◽  
Low Level
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