A Comparative Study on Improvement of Image Compression Method using Hybrid DCT - DWT Techniques with Huffman Encoding for Wireless Sensor Network Application

Nowadays, the demands on the usage of wireless network are increasing rapidly from year to year. Wireless network is a large scale of area where many nodes are connecting to each other to communicate using a device. Primarily, wireless network also tend to be as a link to transmit and receive any multimedia such as image, sound, video, document and etc. In order to receive the transmitted media correctly, most type of media must be compressed before being transmitted and decompressed after being received by the device or else the device used must have the ability to read the media in a compressed way. In this paper, a hybrid compression of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) with Huffman encoding technique are proposed for Wireless Sensor Network (WSN) application. Data compression is very useful to remove the redundant data and reduce the size of image. After conducting a comprehensive observation, it is found that hybrid compression is suitable due to the process consist of the combination of multiple compression techniques which suits for Wireless Sensor Network’s application focusing on ZigBee platform

Image Compression Using Run Length Encoding (RLE)

The goal of image compression is to remove the redundancies by minimizing the number of bits required to represent an image. It is used for reducing the redundancy that is nothing but avoiding the duplicate data. It also reduces the storage memory to load an image. Image Compression algorithm can be Lossy or Lossless. In this paper, DCT and DWT based image compression algorithms have been implemented using MATLAB platform. Then, the improvement of image compression through Run Length Encoding (RLE) has been achieved. The three images namely Baboon, Lena and Pepper have been taken as test images for implementing the techniques. Various image objective metrics namely compression ratio, PSNR and MSE have been calculated. It has been observed from the results that RLE based image compression achieves higher compression ratio as compared with DCT and DWT based image compression algorithms

A Multi-Level Enhanced Color Image Compression Algorithm using SVD & DCT

Nowadays, computer technology is mostly concerned with storage capacity and performance. Compression of digital images has become a fundamental aspect of their transmission and storage. Due to storage and bandwidth constraints, it has become necessary to compress images before to transmission and storage. Not only can image compression techniques help to reduce storage space requirements, but they also aid increase transmission bandwidth. Color images are in trend these days during communication. Most of the researchers have worked only on grayscale images. This research proposes a hybrid approach that encompasses two cutting-edge picture compression algorithms: DCT & SVD. This research involves the advantages and strength of two cutting-edge picture compression algorithms that enable us to compress the color images without additional cost in computation, space and time. Here in this research, for experimental purposes, seam carving image dataset is used. The proposed method's performance is evaluated using the performance evaluation matrices, i.e., Size after Compression, MSE, PSNR, Normalized Co-relation (NC), Percentage Space-Saving, and Compression Ratio. The proposed method performance is also correlated with the two latest image compression techniques, i.e., DCT Block Truncation (DCTBT) and Discrete Cosine Transform - Vector Quantization (DCT-VQ). The findings show that the suggested hybrid color image compression approach is superior to existing compression according to different performance metrics

Image Compression Using Permanent Neural Networks for Predicting Compact Discrete Cosine Transform Coefficients

This study proposes a new image compression technique that produces a high compression ratio yet consumes low execution times. Since many of the current image compression algorithms consume high execution times, this technique speeds up the execution time of image compression. The technique is based on permanent neural networks to predict the discrete cosine transform partial coefficients. This can eliminate the need to generate the discrete cosine transformation every time an image is compressed. A compression ratio of 94% is achieved while the average decompressed image peak signal to noise ratio and structure similarity image measure are 22.25 and 0.65 respectively. The compression time can be neglected when compared to other reported techniques because the only needed process in the compression stage is to use the generated neural network model to predict the few discrete cosine transform coefficients

TSAR: Secure Transfer OF High Resolution Art Images

International audienceThe EROS (European Research Open System) database hosted at the Centre de Recherche et de Restauration des Musées de France (C2RMF) is one of the largest database in the world of Cultural Heritage that is widely recognized for its high resolution images. The French research project TSAR (Transfert Sécurisé d'images d'Art haute Resolution) aims to give the possibility to open this huge amount of art images in a secure and efficient way. For this purpose, we use a mixture of techniques to assure the security of the data involving cryptography and watermarking techniques as well as multi-resolution compression scheme together with a region-level representation. These algorithms are especially optimized for high resolution art images. In particular, this means that the quality of the transmitted images have to be not reduced, implying the use of lossless coding techniques. In this paper we present an overall scheme that provides an efficient, consistent solution for secure data browsing, viewing and transmitting, adoptable by any Cultural Heritage institution

Efficient Encoding of Wireless Capsule Endoscopy Images Using Direct Compression of Colour Filter Array Images

Since its invention in 2001, wireless capsule endoscopy (WCE) has played an important role in the endoscopic examination of the gastrointestinal tract. During this period, WCE has undergone tremendous advances in technology, making it the first-line modality for diseases from bleeding to cancer in the small-bowel. Current research efforts are focused on evolving WCE to include functionality such as drug delivery, biopsy, and active locomotion. For the integration of these functionalities into WCE, two critical prerequisites are the image quality enhancement and the power consumption reduction. An efficient image compression solution is required to retain the highest image quality while reducing the transmission power. The issue is more challenging due to the fact that image sensors in WCE capture images in Bayer Colour filter array (CFA) format. Therefore, standard compression engines provide inferior compression performance. The focus of this thesis is to design an optimized image compression pipeline to encode the capsule endoscopic (CE) image efficiently in CFA format. To this end, this thesis proposes two image compression schemes. First, a lossless image compression algorithm is proposed consisting of an optimum reversible colour transformation, a low complexity prediction model, a corner clipping mechanism and a single context adaptive Golomb-Rice entropy encoder. The derivation of colour transformation that provides the best performance for a given prediction model is considered as an optimization problem. The low complexity prediction model works in raster order fashion and requires no buffer memory. The application of colour transformation yields lower inter-colour correlation and allows the efficient independent encoding of the colour components. The second compression scheme in this thesis is a lossy compression algorithm with a integer discrete cosine transformation at its core. Using the statistics obtained from a large dataset of CE image, an optimum colour transformation is derived using the principal component analysis (PCA). The transformed coefficients are quantized using optimized quantization table, which was designed with a focus to discard medically irrelevant information. A fast demosaicking algorithm is developed to reconstruct the colour image from the lossy CFA image in the decoder. Extensive experiments and comparisons with state-of-the-art lossless image compression methods establish the superiority of the proposed compression methods as simple and efficient image compression algorithm. The lossless algorithm can transmit the image in a lossless manner within the available bandwidth. On the other hand, performance evaluation of lossy compression algorithm indicates that it can deliver high quality images at low transmission power and low computation costs