scholarly journals A Novel Psychovisual Threshold on Large DCT for Image Compression

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Nur Azman Abu ◽  
Ferda Ernawan
Keyword(s):  
Image Compression ◽  
Research Study ◽  
Experimental Results ◽  
Human Visual Perception ◽  
Image Block ◽  
Quality Image ◽  
Optimal Quantization ◽  
Transform Coefficients ◽  
Jpeg Image Compression

A psychovisual experiment prescribes the quantization values in image compression. The quantization process is used as a threshold of the human visual system tolerance to reduce the amount of encoded transform coefficients. It is very challenging to generate an optimal quantization value based on the contribution of the transform coefficient at each frequency order. The psychovisual threshold represents the sensitivity of the human visual perception at each frequency order to the image reconstruction. An ideal contribution of the transform at each frequency order will be the primitive of the psychovisual threshold in image compression. This research study proposes a psychovisual threshold on the large discrete cosine transform (DCT) image block which will be used to automatically generate the much needed quantization tables. The proposed psychovisual threshold will be used to prescribe the quantization values at each frequency order. The psychovisual threshold on the large image block provides significant improvement in the quality of output images. The experimental results on large quantization tables from psychovisual threshold produce largely free artifacts in the visual output image. Besides, the experimental results show that the concept of psychovisual threshold produces better quality image at the higher compression rate than JPEG image compression.

Fast Vector Quantization Encoding Algorithms for Image Compression

2011 ◽  
pp. 162-174
Author(s):  
Ahmed Swilem
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Experimental Results ◽  
Search Process ◽  
Compression Method ◽  
Image Block ◽  
Fast Encoding ◽  
Search Area ◽  
Significant Acceleration ◽  
Block Data

Vector quantization (VQ) is a well-known compression method. In the encoding phase, given a block represented as a vector, searching the closest codeword in the codebook is a time-consuming task. In this paper, two fast encoding algorithms for VQ are proposed. To reduce the search area and accelerate the search process, the first algorithm utilizes three significant features of a vector that are, the norm, and two projection angles to two projection axes. The second algorithm uses the first two features as in the first algorithm with the projection value of the vector to the second projection axe. The algorithms allow significant acceleration in the encoding process. Experimental results are presented on image block data. These results confirm the effectiveness of the proposed algorithms.

Research on Image Feature Information of Resisting JPEG Compression

2011 ◽  
Vol 121-126 ◽  
pp. 2190-2194
Author(s):  
Chao Kui Wu ◽  
Liang Yong Huang
Keyword(s):  
Image Compression ◽  
Quality Factor ◽  
Discrete Cosine Transform ◽  
Jpeg Compression ◽  
Experimental Results ◽  
Image Feature ◽  
Cosine Transform ◽  
The Third ◽  
Feature Information

To solve the image feature information on the robustness of JPEG compression problems, we use the third layer of wavelet of images to decompose the approximation sub-band coefficients as the image feature information, analyze the characteristics of the discrete cosine transform in JPEG compression so as to compare the robustness of image feature information under different quality of JPEG compression. The experimental results show that: make the image compression quality factor more than the standard JPEG, the approximate sub-band coefficients of image wavelet as the image feature information has better robustness, and their difference is less than or equal to a smaller threshold.

A NONUNIFORM HIGH-QUALITY IMAGE COMPRESSION METHOD TO PRESERVE USER-SPECIFIED COMPRESSION RATIO

2011 ◽  
Vol 11 (03) ◽  
pp. 355-375 ◽  
Author(s):  
MOHAMMAD REZA BONYADI ◽  
MOHSEN EBRAHIMI MOGHADDAM
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Linear Mapping ◽  
Compression Method ◽  
High Quality ◽  
Compressed Images ◽  
High Quality Image ◽  
Quality Image

Most of image compression methods are based on frequency domain transforms that are followed by a quantization and rounding approach to discard some coefficients. It is obvious that the quality of compressed images highly depends on the manner of discarding these coefficients. However, finding a good balance between image quality and compression ratio is an important issue in such manners. In this paper, a new lossy compression method called linear mapping image compression (LMIC) is proposed to compress images with high quality while the user-specified compression ratio is satisfied. This method is based on discrete cosine transform (DCT) and an adaptive zonal mask. The proposed method divides image to equal size blocks and the structure of zonal mask for each block is determined independently by considering its gray-level distance (GLD). The experimental results showed that the presented method had higher pick signal to noise ratio (PSNR) in comparison with some related works in a specified compression ratio. In addition, the results were comparable with JPEG2000.

Underwater Optical Image Coding for Marine Health Monitoring Based on DCT

2019 ◽  
Vol 14 ◽  
Author(s):  
Mohammad Kazem Moghimi ◽  
Farahnaz Mohanna
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
Patch Size ◽  
Image Block ◽  
Compression Process ◽  
Edge Preservation ◽  
Underwater Communications ◽  
Main Technique ◽  
Reliable Communications

Background: Optical imaging in underwater environment to monitor marine objects is now a hot topic of research which can be used for environmental healthcare systems through the underwater ecosystem. Among different areas of research, image coding techniques are widely applied to compress data for reliable communications such that we know one of the most challenges in underwater communications is to have a low bit rate in acoustic links particularly while doing imaging in deep waters (in this condition, light needed for imaging is provided by a battery). Methods: Two dimensional Discrete Cosine Transform (2D-DCT) is the main technique that we use for image compression here, on which we test two different patch sizes in 2D-DCT to study of the patch size effect on quality of compression, execution time and preservation ability of high-frequency information in edges. Results: The results clearly show results that a larger patch size can always be better in terms of computational complexity, quality of coded images and also edge preservation when we use DCT for the compression process. Discussion: Although this research approves the approach of JPEG codec once again for using the largest sub-image block in image compression (in terms of similarity and complexity), however use of an edge preservation factor is a new finding for our research. On the other hand, using the biggest patch size is not a general approach for all image processing applications, because some researches have shown that smaller patch may be more effective for some other applications.

Perceptual Image Compression with Block-Level Just Noticeable Difference Prediction

2021 ◽  
Vol 16 (4) ◽  
pp. 1-15
Author(s):  
Tao Tian ◽  
Hanli Wang ◽  
Sam Kwong ◽  
C.-C. Jay Kuo
Keyword(s):  
Image Compression ◽  
Prediction Model ◽  
Structural Similarity ◽  
Just Noticeable Difference ◽  
Test Image ◽  
Image Block ◽  
Otsu Method ◽  
Block Level ◽  
Transform Coefficients ◽  
Perceptual Image Compression

A block-level perceptual image compression framework is proposed in this work, including a block-level just noticeable difference (JND) prediction model and a preprocessing scheme. Specifically speaking, block-level JND values are first deduced by utilizing the OTSU method based on the variation of block-level structural similarity values between two adjacent picture-level JND values in the MCL-JCI dataset. After the JND value for each image block is generated, a convolutional neural network–based prediction model is designed to forecast block-level JND values for a given target image. Then, a preprocessing scheme is devised to modify the discrete cosine transform coefficients during JPEG compression on the basis of the distribution of block-level JND values of the target test image. Finally, the test image is compressed by the max JND value across all of its image blocks in the light of the initial quality factor setting. The experimental results demonstrate that the proposed block-level perceptual image compression method is able to achieve 16.75% bit saving as compared to the state-of-the-art method with similar subjective quality. The project page can be found at https://mic.tongji.edu.cn/43/3f/c9778a148287/page.htm.

PENGARUH KUALITAS LAYANAN DAN NILAI PELANGGAN TERHADAP KEPUASAN PELANGGAN TOKO SERBA ADA MATAHARI DI PONDOK GEDE PLAZA KOTA BEKASI

2017 ◽  
Vol 5 (3) ◽  
Author(s):  
Drs. Iwan Kurniawan Subagja, SE., MM. ◽  
Adista Fitriani
Keyword(s):  
Regression Analysis ◽  
Customer Satisfaction ◽  
Service Quality ◽  
Customer Value ◽  
Research Study ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Analysis Technique ◽  
Study Population

Business competition is currently increasingly stringent requires that marketers develop marketing strategy that is telling. Companies that win business competition can solidify the company's position to survive in the future. This research study aims to analyze the effect of service quality and customer value on customer satisfaction of Multipurpose Shop Matahari in Pondok Gede Plaza simultaneously and partially. The study population is all customers of multipurpose store Matahari. This research uses multiple linear regression analysis technique, with survey data and questionnaire to 130 respondents. The results of this study indicate that the quality of service and customer value positively and significantly affect the customer satisfaction, service quality and customer value positively and partially significant to customer satisfaction.

scholarly journals 3D DCT Based Image Compression Method for the Medical Endoscopic Application

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1817
Author(s):  
Jiawen Xue ◽  
Li Yin ◽  
Zehua Lan ◽  
Mingzhu Long ◽  
Guolin Li ◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Blocking Artifacts ◽  
Compression Method ◽  
High Compression Ratio ◽  
Multiplication Operation ◽  
3D Data ◽  
Wireless Capsule

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

2021 ◽  
Vol 40 (5) ◽  
pp. 9361-9382 ◽  
Author(s):  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Faisal Jamil ◽  
Do-Hyeun Kim
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Resource Planning ◽  
Experimental Results ◽  
Quality Prediction ◽  
Classification Models ◽  
Hybrid Features ◽  
Social Media Data ◽  
Media Data

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.

scholarly journals Identification of the State of Electrical Appliances with the Use of a Pulse Signal Generator

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 673
Author(s):  
Augustyn Wójcik ◽  
Piotr Bilski ◽  
Robert Łukaszewski ◽  
Krzysztof Dowalla ◽  
Ryszard Kowalik
Keyword(s):  
Measurement System ◽  
Cross Correlation ◽  
Pulse Signal ◽  
Signal Generator ◽  
Experimental Results ◽  
The Novel ◽  
End User ◽  
Standard Classification ◽  
Electrical Appliance

The paper presents the novel HF-GEN method for determining the characteristics of Electrical Appliance (EA) operating in the end-user environment. The method includes a measurement system that uses a pulse signal generator to improve the quality of EA identification. Its structure and the principles of operation are presented. A method for determining the characteristics of the current signals’ transients using the cross-correlation is described. Its result is the appliance signature with a set of features characterizing its state of operation. The quality of the obtained signature is evaluated in the standard classification task with the aim of identifying the particular appliance’s state based on the analysis of features by three independent algorithms. Experimental results for 15 EAs categories show the usefulness of the proposed approach.

scholarly journals Contour Detection for Fibre of Preserved Szechuan Pickle Based on Dilated Convolution

2019 ◽  
Vol 9 (13) ◽  
pp. 2684 ◽  
Author(s):  
Hongyang Li ◽  
Lizhuang Liu ◽  
Zhenqi Han ◽  
Dan Zhao
Keyword(s):  
Edge Detection ◽  
State Of The Art ◽  
Contour Detection ◽  
Experimental Results ◽  
Contour Method ◽  
Class Differences ◽  
Dilated Convolution ◽  
The Mean ◽  
Art Performance

Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automatic peeling device, is studied. The fibre contour is a kind of non-salient contour, characterized by big intra-class differences and small inter-class differences, meaning that the feature of the contour is not discriminative. The method called dilated-holistically-nested edge detection (Dilated-HED) is proposed to detect the fibre contour, which is built based on the HED network and dilated convolution. The experimental results for our dataset show that the Pixel Accuracy (PA) is 99.52% and the Mean Intersection over Union (MIoU) is 49.99%, achieving state-of-the-art performance.

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