Quantization Encoding Algorithm Based Satellite Image Compression

2017 ◽  
Vol 8 (3) ◽  
pp. 740
Author(s):  
Anand M ◽  
V. Mathivananr
Keyword(s):  
Image Compression ◽  
Satellite Images ◽  
Satellite Image ◽  
Region Of Interest ◽  
Digital Data ◽  
Storage Space ◽  
Compression Process ◽  
Compression Technique ◽  
Transmission Bandwidth ◽  
Image Technique

In the field of digital data there is a demand in bandwidth for the transmission of the videos and images all over the worlds. So in order to reduce the storage space in the field of image applications there is need for the image compression process with lesser transmission bandwidth. So in this paper we are proposing a new image compression technique for the compression of the satellite images by using the Region of Interest (ROI) based on the lossy image technique called the Quantization encoding algorithm for the compression. The performance of our method can be evaluated and analyzing the PSNR values of the output images.

scholarly journals Ideal Huffman Code for Lossless Image Compression for Ubiquitous Access

2018 ◽  
Vol 12 (2) ◽  
pp. 765 ◽  
Author(s):  
T Kavitha ◽  
K. Jayasankar
Keyword(s):  
Image Compression ◽  
Smart Phone ◽  
Computation Time ◽  
Digital Data ◽  
Compression Technique ◽  
Lossless Image Compression ◽  
Coding Efficiency ◽  
Average Computation Time ◽  
Ubiquitous Access ◽  
Cloud Storage Environment

<p>Compression technique is adopted to solve various big data problems such as storage and transmission. The growth of cloud computing and smart phone industries has led to generation of huge volume of digital data. Digital data can be in various forms as audio, video, images and documents. These digital data are generally compressed and stored in cloud storage environment. Efficient storing and retrieval mechanism of digital data by adopting good compression technique will result in reducing cost. The compression technique is composed of lossy and lossless compression technique. Here we consider Lossless image compression technique, minimizing the number of bits for encoding will aid in improving the coding efficiency and high compression. Fixed length coding cannot assure in minimizing bit length. In order to minimize the bits variable Length codes with prefix-free codes nature are preferred. However the existing compression model presented induce high computing overhead, to address this issue, this work presents an ideal and efficient modified Huffman technique that improves compression factor up to 33.44% for Bi-level images and 32.578% for Half-tone Images. The average computation time both encoding and decoding shows an improvement of 20.73% for Bi-level images and 28.71% for Half-tone images. The proposed work has achieved overall 2% increase in coding efficiency, reduced memory usage of 0.435% for Bi-level images and 0.19% for Half-tone Images. The overall result achieved shows that the proposed model can be adopted to support ubiquitous access to digital data.</p>

scholarly journals REGION OF INTEREST DETECTION BASED ON HISTOGRAM SEGMENTATION FOR SATELLITE IMAGE

2016 ◽  
Vol XLI-B7 ◽  
pp. 249-255
Author(s):  
Warinthorn Kiadtikornthaweeyot ◽  
Adrian R. L. Tatnall
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Region Of Interest ◽  
Satellite Imaging ◽  
Proposed Model ◽  
High Resolution Satellite Images ◽  
Image Area ◽  
Interest Detection

High resolution satellite imaging is considered as the outstanding applicant to extract the Earth’s surface information. Extraction of a feature of an image is very difficult due to having to find the appropriate image segmentation techniques and combine different methods to detect the Region of Interest (ROI) most effectively. This paper proposes techniques to classify objects in the satellite image by using image processing methods on high-resolution satellite images. The systems to identify the ROI focus on forests, urban and agriculture areas. The proposed system is based on histograms of the image to classify objects using thresholding. The thresholding is performed by considering the behaviour of the histogram mapping to a particular region in the satellite image. The proposed model is based on histogram segmentation and morphology techniques. There are five main steps supporting each other; Histogram classification, Histogram segmentation, Morphological dilation, Morphological fill image area and holes and ROI management. The methods to detect the ROI of the satellite images based on histogram classification have been studied, implemented and tested. The algorithm is be able to detect the area of forests, urban and agriculture separately. The image segmentation methods can detect the ROI and reduce the size of the original image by discarding the unnecessary parts.

scholarly journals Two-dimensional satellite image compression using compressive sensing

2022 ◽  
Vol 12 (1) ◽  
pp. 311
Author(s):  
Gunasheela Keragodu Shivanna ◽  
Haranahalli Shreenivasamurthy Prasantha
Keyword(s):  
Image Compression ◽  
Compressive Sensing ◽  
Satellite Image ◽  
Research Community ◽  
Reconstruction Accuracy ◽  
Promising Technique ◽  
Compression Technique ◽  
Satellite Imaging ◽  
Compression Algorithms ◽  
Ground Station

Compressive sensing is receiving a lot of attention from the image processing research community as a promising technique for image recovery from very few samples. The modality of compressive sensing technique is very useful in the applications where it is not feasible to acquire many samples. It is also prominently useful in satellite imaging applications since it drastically reduces the number of input samples thereby reducing the storage and communication bandwidth required to store and transmit the data into the ground station. In this paper, an interior point-based method is used to recover the entire satellite image from compressive sensing samples. The compression results obtained are compared with the compression results from conventional satellite image compression algorithms. The results demonstrate the increase in reconstruction accuracy as well as higher compression rate in case of compressive sensing-based compression technique.

Vector Quantization for Satellite Image Compression

2016 ◽  
Vol 5 ◽  
pp. 22
Author(s):  
Sanjith Sathya Joseph ◽  
R. Ganesan
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Satellite Images ◽  
Signal To Noise Ratio ◽  
Satellite Image ◽  
Signal To Noise ◽  
Transmission Process ◽  
Noise Ratio ◽  
Time Required

Image compression is the process of reducing the size of a file without humiliating the quality of the image to an unacceptable level by Human Visual System. The reduction in file size allows as to store more data in less memory and speed up the transmission process in low bandwidth also, in case of satellite images it reduces the time required for the image to reach the ground station. In order to increase the transmission process compression plays an important role in remote sensing images.  This paper presents a coding scheme for satellite images using Vector Quantization. And it is a well-known technique for signal compression, and it is also the generalization of the scalar quantization.  The given satellite image is compressed using VCDemo software by creating codebooks for vector quantization and the quality of the compressed and decompressed image is compared by the Mean Square Error, Signal to Noise Ratio, Peak Signal to Noise Ratio values.

An improved medical image compression technique with lossless region of interest

Optik ◽  
2015 ◽  
Vol 126 (21) ◽  
pp. 2825-2831 ◽  
Author(s):  
Zhiyong Zuo ◽  
Xia Lan ◽  
Lihua Deng ◽  
Shoukui Yao ◽  
Xiaoping Wang
Keyword(s):  
Image Compression ◽  
Medical Image ◽  
Region Of Interest ◽  
Compression Technique ◽  
Medical Image Compression

scholarly journals Comparitive Analysis and Findings on Dct & Lbg Compression Techniques

2020 ◽  
Vol 9 (06) ◽  
pp. 25075-25084
Author(s):  
Mr. Moayad Al Falahi ◽  
Dr. Janaki Sivakumar
Keyword(s):  
College Students ◽  
User Interface ◽  
Image Compression ◽  
Graphical User Interface ◽  
Lossless Compression ◽  
Compression Process ◽  
Compression Technique ◽  
Lbg Algorithm ◽  
Before And After ◽  
Bulk Processing

The main objective of this project is to develop an application to find the best compression technique to store Muscat College students' photographs in less storage. MATLAB software will be used to develop a Graphical User Interface GUI application and implement two image compression techniques which are lossless compression using the DCT algorithm and lossy compression using the LBG algorithm.  The application shall allow the user to select and test a sample image by applying both these techniques for any student image he\she selects in order to compare the results by display the image after compression and the histogram to find which the most suitable compression technique is. Also, the application shall show the size of images before and after applying the compression process and show the compression ratio and relative data redundancy of compressed image/images. The main functionality is that the application shall allow the user to do bulk processing to apply image enhancement and image compression technique to enhance and compress all the photographs of students and store them in less space.

scholarly journals Medical Image Compression by Optimal Filter Coefficients Aided Transforms using Modified Rider Optimization Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 5383-5393
Keyword(s):  
Image Compression ◽  
Optimization Algorithm ◽  
Medical Image ◽  
Region Of Interest ◽  
Region Growing ◽  
Superior Performance ◽  
Discrete Wavelet ◽  
Compression Process ◽  
Medical Image Compression ◽  
Segmentation Image

Owing to a large amount of images, image compression is requisite for minimizing the redundancies in image, and it offers efficient transmission and archiving of images. This paper presents a novel medical image compression model using intelligent techniques. The adopted medical image compression comprises of three major steps such as, Segmentation, Image compression, and Image decompression. Initially, the Region of Interest (ROI) and Non-ROI regions of the image are split by means of a Segmentation procedure using Modified Region Growing (MRG) algorithm. Moreover, the image compression process begins which is varied for both ROI and Non-ROI regions. On considering the ROI regions, the compression is carried out by Discrete Cosine Transform (DCT) model and SPIHT encoding method, whereas the compression of Non-ROI region is carried out by Discrete Wavelet Transform (DWT) and Merge-based Huffman encoding (MHE) methods. As a main contribution, this paper intends to deploy the optimized filter coefficients in both DCT and DWT techniques. Here, the optimization of both filter coefficients is performed using Modified Rider Optimization Algorithm (ROA) called Improvised Steering angle and Gear-based ROA (ISG-ROA). In the final step, decompression is done by implementing the reverse concept of compression process with similar optimized coefficients. The filter coefficients are tuned in such a way that the Compression Ratio (CR) should be minimum. In addition, the comparative analysis over the state-of-the-art models proves the superior performance of the proposed model.

scholarly journals Fractal Image Compression Using Canonical Huffman Coding

2020 ◽  
Vol 15 (1) ◽  
pp. 91-105
Author(s):  
Shree Ram Khaitu ◽  
Sanjeeb Prasad Panday
Keyword(s):  
Image Compression ◽  
Lossy Compression ◽  
Digital Data ◽  
Huffman Coding ◽  
Compression Scheme ◽  
Fractal Image Compression ◽  
Compression Technique ◽  
Fractal Compression ◽  
Fractal Image ◽  
Entropy Encoding

 Image Compression techniques have become a very important subject with the rapid growth of multimedia application. The main motivations behind the image compression are for the efficient and lossless transmission as well as for storage of digital data. Image Compression techniques are of two types; Lossless and Lossy compression techniques. Lossy compression techniques are applied for the natural images as minor loss of the data are acceptable. Entropy encoding is the lossless compression scheme that is independent with particular features of the media as it has its own unique codes and symbols. Huffman coding is an entropy coding approach for efficient transmission of data. This paper highlights the fractal image compression method based on the fractal features and searching and finding the best replacement blocks for the original image. Canonical Huffman coding which provides good fractal compression than arithmetic coding is used in this paper. The result obtained depicts that Canonical Huffman coding based fractal compression technique increases the speed of the compression and has better PNSR as well as better compression ratio than standard Huffman coding.  

scholarly journals Survey of Hybrid Image Compression Techniques

2017 ◽  
Vol 7 (4) ◽  
pp. 2206
Author(s):  
Emy Setyaningsih ◽  
Agus Harjoko
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Lossless Compression ◽  
Quality Data ◽  
Compression Method ◽  
Compression Process ◽  
High Quality ◽  
Compression Technique ◽  
Technique Development

A compression process is to reduce or compress the size of data while maintaining the quality of information contained therein. This paper presents a survey of research papers discussing improvement of various hybrid compression techniques during the last decade. A hybrid compression technique is a technique combining excellent properties of each group of methods as is performed in JPEG compression method. This technique combines lossy and lossless compression method to obtain a high-quality compression ratio while maintaining the quality of the reconstructed image. Lossy compression technique produces a relatively high compression ratio, whereas lossless compression brings about high-quality data reconstruction as the data can later be decompressed with the same results as before the compression. Discussions of the knowledge of and issues about the ongoing hybrid compression technique development indicate the possibility of conducting further researches to improve the performance of image compression method.

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