Lossless and Lossy Polynomial Image Compression
(1)
Publication Date
Fri Aug 23 2013
Journal Name
International Journal Of Computer Applications
(3)
Publication Date
Wed Jun 01 2022
Journal Name
V. International Scientific Congress Of Pure, Applied And Technological Sciences
Publication Date
Thu Jan 01 2015
Journal Name
International Journal Of Computer Science And Mobile Computing
Publication Date
Wed Jul 25 2018
Journal Name
International Journal Of Engineering Trends And Technology
(1)
Publication Date
Fri May 16 2014
Journal Name
International Journal Of Computer Applications
(4)
Publication Date
Wed Dec 01 2021
Journal Name
Journal Of Physics: Conference Series
Art Image Compression Based on Lossless LZW Hashing Ciphering Algorithm
Abstract<p>Color image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images and obtaining a low bit rate by compressing the matrix resulting from the scalar quantization process (reducing the number of bits from 24 to 8 bits) using displacement coding and then compressing the remainder using the Mabel ZF algorithm Welch LZW. The proposed methodology maintains the quality of the reconstructed image. Macroscopic and </p>
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(5)
(2)
Publication Date
Wed Jul 06 2022
Journal Name
Journal Of Al-qadisiyah For Computer Science And Mathematics
Publication Date
Sun May 01 2016
Journal Name
International Journal Of Computer Applications
Publication Date
Sat Aug 01 2015
Journal Name
International Journal Of Computer Science And Mobile Computing
Publication Date
Tue Aug 10 2021
Journal Name
Design Engineering
Lossy Image Compression Using Hybrid Deep Learning Autoencoder Based On kmean Clusteri
Image compression
Convolutional Autoencoder (CAE)
k-mean algorithm
PSNR
Compression Rate (CR)
MSE
Clustering
CLIC
Kodak
deep learning
lossy
Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye
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