scholarly journals Design and implementation of non-uniform quantizers for discrete input samples and its application to an image processing algorithm

2017 ◽  
Vol 30 (3) ◽  
pp. 417-427
Author(s):  
Nikola Simic ◽  
Zoran Peric ◽  
Milan Savic
Keyword(s):  
Image Processing ◽  
Standard Test ◽  
Grayscale Image ◽  
Image Processing Algorithm ◽  
Design And Implementation ◽  
Unit Variance ◽  
Signal Value ◽  
Uniform Quantization ◽  
Input Source ◽  
Discrete Values

This paper describes an algorithm for grayscale image compression based on non-uniform quantizers designed for discrete input samples. Non-uniform quantization is performed in two steps for unit variance, whereas design is done by introducing a discrete variance. The best theoretical and experimental results are obtained for those discrete values of variance which provide the operating range of quantizer located in the vicinity of maximal signal value that can appear on the entrance. The experiment is performed by applying proposed quantizers for compression of standard test grayscale images as a classic example of discrete input source. The proposed fixed non-uniform quantizers, designed for discrete input samples, provide up to 4.93 [dB] higher PSQNR compared to the fixed piecewise uniform quantizers designed for discrete input samples.

AN IMAGE PROCESSING ALGORITHM TO FIND EDGES IN GRAYSCALE IMAGE

2017 ◽  
Vol 17 (4) ◽  
pp. 781-788 ◽  
Author(s):  
Ratri Dwi Atmaja ◽  
Ledya Novamizanti ◽  
Junartho Halomoan ◽  
Muhammad Ary Murti
Keyword(s):  
Image Processing ◽  
Processing Algorithm ◽  
Grayscale Image ◽  
Image Processing Algorithm

Design and Implementation of a New Efficient Embedded Image System

2014 ◽  
Vol 667 ◽  
pp. 196-200
Author(s):  
Jie Zhang ◽  
Zhi Jian Dai ◽  
Shu Ying Cheng ◽  
Pei Jie Lin
Keyword(s):  
Image Processing ◽  
High Performance ◽  
Low Cost ◽  
Digital Camera ◽  
Data Access ◽  
Experimental Result ◽  
Image Processing Algorithm ◽  
Image System ◽  
Design And Implementation ◽  
Embedded Image

This article presents a new design and implementation of a robust and efficient embedded image system which can perform image acquisition, data saving and communication. The system hardware consists of processors of CPLD and ARM, an OV7620 digital camera, an ISSI SRAM and some peripheral interfaces. The key part of the software is the usage of analogous DMA storage and processing technology which makes the image system have a higher efficiency. A memory allocation algorithm is developed to maximize parallel data access and make the utmost use of ARM’s processing ability, thus improving the system performance. Furthermore, a simple practical binary image processing algorithm is developed to enhance the performance of image processing in this article. The experimental result shows that the proposed design approach for a low cost embedded image system can achieve high performance, robustness and efficiency.

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.

Internal quality evaluation of chestnut using nuclear magnetic resonance

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Soo Hyun Park ◽  
Sang Ha Noh ◽  
Michael J. McCarthy ◽  
Seong Min Kim
Keyword(s):  
Image Processing ◽  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Processing Algorithm ◽  
Internal Quality ◽  
Soluble Solid Content ◽  
Mr Images ◽  
Image Processing Algorithm ◽  
Internal Defects ◽  
Nuclear Magnetic

AbstractThis study was carried out to develop a prediction model for soluble solid content (SSC) of intact chestnut and to detect internal defects using nuclear magnetic resonance (NMR) relaxometry and magnetic resonance imaging (MRI). Inversion recovery and Carr–Purcell–Meiboom–Gill (CPMG) pulse sequences used to determine the longitudinal (T1) and transverse (T2) relaxation times, respectively. Partial least squares regression (PLSR) was adopted to predict SSCs of chestnuts with NMR data and histograms from MR images. The coefficient of determination (R2), root mean square error of prediction (RMSEP), ratio of prediction to deviation (RPD), and the ratio of error range (RER) of the optimized model to predict SSC were 0.77, 1.41 °Brix, 1.86, and 11.31 with a validation set. Furthermore, an image-processing algorithm has been developed to detect internal defects such as decay, mold, and cavity using MR images. The classification applied with the developed image processing algorithm was over 94% accurate to classify. Based on the results obtained, it was determined that the NMR signal could be applied for grading several levels by SSC, and MRI could be used to evaluate the internal qualities of chestnuts.

Simulation of classroom student behavior recognition based on PSO-kNN algorithm and emotional image processing

2020 ◽  
pp. 1-11
Author(s):  
Shilong Wu
Keyword(s):  
Image Processing ◽  
Emotion Recognition ◽  
Student Behavior ◽  
Classroom Behavior ◽  
Feature Recognition ◽  
Pso Algorithm ◽  
Cluster Center ◽  
Image Processing Algorithm ◽  
Behavior Recognition ◽  
Key Frame

Students’ classroom behavior recognition and emotion recognition effects directly determine the degree of teachers’ control of the classroom teaching process. At present, teachers and students belong to two groups in traditional teaching, and teachers cannot effectively mobilize students’ learning emotions. In order to improve the teaching effect, this paper combines the PSO algorithm and the KNN algorithm to obtain the PSO-KNN joint algorithm, and combines with the emotional image processing algorithm to construct an artificial intelligence-based classroom student behavior recognition model. Moreover, based on the image processing technology, this paper uses key frame detection for feature recognition, and this paper improves the recognition process based on the inter-frame similarity measurement algorithm and initial cluster center selection in the key frame extraction method of clustering. In addition, this paper analyzes the effect of the model constructed on the behavior recognition and emotion recognition of students. The research results show that the joint algorithm constructed in this paper has a high accuracy rate for students’ emotion recognition and behavior recognition, and can meet the actual teaching needs.

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