Audio compression using transforms and high order entropy encoding

Digital audio is required to transmit large sizes of audio information through the most common communication systems; in turn this leads to more challenges in both storage and archieving. In this paper, an efficient audio compressive scheme is proposed, it depends on combined transform coding scheme; it is consist of i) bi-orthogonal (tab 9/7) wavelet transform to decompose the audio signal into low & multi high sub-bands, ii) then the produced sub-bands passed through DCT to de-correlate the signal, iii) the product of the combined transform stage is passed through progressive hierarchical quantization, then traditional run-length encoding (RLE), iv) and finally LZW coding to generate the output mate bitstream. The measures Peak signal-to-noise ratio (PSNR) and compression ratio (CR) were used to conduct a comparative analysis for the performance of the whole system. Many audio test samples were utilized to test the performance behavior; the used samples have various sizes and vary in features. The simulation results appear the efficiency of these combined transforms when using LZW within the domain of data compression. The compression results are encouraging and show a remarkable reduction in audio file size with good fidelity

Image Steganography by Using Multiwavelet Transform

Steganography is the art of secret communication. Its purpose is to hide the presence of information, using, for example, images as covers. The frequency domain is well suited for embedding in image, since hiding in this frequency domain coefficients is robust to many attacks. This paper proposed hiding a secret image of size equal to quarter of the cover one. Set Partitioning in Hierarchal Trees (SPIHT) codec is used to code the secret image to achieve security. The proposed method applies Discrete Multiwavelet Transform (DMWT) for cover image. The coded bit stream of the secret image is embedded in the high frequency subbands of the transformed cover one. A scaling factors ? and ? in frequency domain control the quality of the stego images. The proposed algorithm is compared with wavelet based algorithm which shows a favorable results in terms of PSNR reaches to 18 dB

A proposal for the management of data driven services in smart manufacturing scenarios

205 p.This research work focuses on Industrial Big Data Services (IBDS) Providers, a specialization of ITServices Providers. IBDS Providers constitute a fundamental agent in Smart Manufacturing scenarios,given the wide spectrum of complex technological challenges involved in the adoption of the requireddata-related IT by manufacturers aiming at shifting their businesses towards Smart Manufacturing. Theoverarching goal of this research work is to provide contributions that (a) help the business sector ofIBDS Providers to manage their collaboration projects with manufacturing partners in order to deploy therequired data-driven services in Smart Manufacturing scenarios, and (b) adapt and extend existingconceptual, methodological, and technological proposals in order to include those practical elements thatfacilitate their use in business contexts. The main contributions of this dissertation focus on three specificchallenges related to the early stages of the data lifecycle, i.e. those stages that ensure the availability ofnew data to exploit, coming from monitored manufacturing facilities: (1) Devising a more efficient datastorage strategy that reduces the costs of the cloud infrastructure required by an IBDS Provider tocentralize and accumulate the massive-scale amounts of data from the supervised manufacturingfacilities; (2) Designing the required architecture for the data capturing and integration infrastructure thatsustains an IBDS Provider's platform; (3) The collaborative design process with partnering manufacturersof the required data-driven services for a specific manufacturing sector

A region-based Principal Component Analysis (PCA) technique for medical image compression

Principal Component Analysis (PCA) is capable of completely decorrelating input data in the transform domain. However, PCA is limited in image compression because there is a need to encode the eigenvectors of the input data and thereby affects the rate-distortion performance. In an effort to improve rate-distortion performance, this work proposed a block-to-row PCA (BTRPCA) algorithm that employs the eigenvectors from the model image of the same image modality coupled with a row vectorization approach. Region-based compression schemes that reduce storage space while preserving the image quality of the region of interest (ROI) are receiving attention due to the increase in medical imaging data. While PCA is inherently limited by its matrix form, the Arbitrary ROI coding (ARC) proposed in this work models the ROI by means of a factorization approach and the arbitrary-shaped ROI contours and NROI are compressed using BTRPCA. In order to minimize user interaction, an automated brain segmentation technique based on midsagittal plane (MSP) and Absolute Difference Map (ADM) is then incorporated into the proposed Automated Arbitrary PCA (AAPCA). The presented result showed that BTRPCA achieves PSNR improvements of up to 10 dB compared to its PCA counterparts. The ARC outperforms JPEG, Embedded Zerotree Wavelet (EZW) and Embedded Block Coding With Optimized Truncation (EBCOT) at all tested bit rates with an average PSNR improvements of 6 dB, 18 dB and 12 dB respectively. Subjective performance analysis was in agreement with the objective performance analysis in which the AAPCA is capable of extending beyond the compression limits of the conventional PCA algorithm and that the quality of the surroundings of ROI is degrading gracefully at bpp as low as 0.25. The research has successfully developed an improved region-based compression scheme for medical images where lossy and lossless compression is implemented in one PCA architecture. Continuation of this study include using different encoding schemes to boost the rate-distortion performance and extraction of multiple ROI

Image Steganography by Using Multiwavelet Transform

Steganography is the art of secret communication. Its purpose is to hide the presence of information, using, for example, images as covers. The frequency domain is well suited for embedding in image, since hiding in this frequency domain coefficients is robust to many attacks. This paper proposed hiding a secret image of size equal to quarter of the cover one. Set Partitioning in Hierarchal Trees (SPIHT) codec is used to code the secret image to achieve security. The proposed method applies Discrete Multiwavelet Transform (DMWT) for cover image. The coded bit stream of the secret image is embedded in the high frequency subbands of the transformed cover one. A scaling factors ? and ? in frequency domain control the quality of the stego images. The proposed algorithm is compared with wavelet based algorithm which shows a favorable results in terms of PSNR reaches to 18 dB

The 1993 Space and Earth Science Data Compression Workshop

The Earth Observing System Data and Information System (EOSDIS) is described in terms of its data volume, data rate, and data distribution requirements. Opportunities for data compression in EOSDIS are discussed

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Evaluation and implementation of an auto-encoder for compression of satellite images in the ScOSA project

The thesis evaluates the efficiency of various autoencoder neural networks for image compression regarding satellite imagery. The results highlight the evaluation and implementation of autoencoder architectures and the procedures required to deploy neural networks to reliable embedded devices. The developed autoencoders evaluated, targeting a ZYNQ 7020 FPGA (Field Programmable Gate Array) and a ZU7EV FPGA

Evaluation and implementation of an auto-encoder for compression of satellite images in the ScOSA project

The thesis evaluates the efficiency of various autoencoder neural networks for image compression regarding satellite imagery. The results highlight the evaluation and implementation of autoencoder architectures and the procedures required to deploy neural networks to reliable embedded devices. The developed autoencoders evaluated, targeting a ZYNQ 7020 FPGA (Field Programmable Gate Array) and a ZU7EV FPGA

Implementation of Lossless Preprocessing Technique for Student Record System

The Implementation of Lossless Preprocessing Technique for Student Record System of the Lyceum of the Philippines University mainly aims to apply an effective data compression algorithm to efficiently store data, improve data transmission via web-based infrastructure and ensure security of records. The system is comprised of the following modules namely: Smart ID Registration Module, Client-Side Application Module and Information Kiosk Module. For enhanced security consideration, GZIPalso known as GNU ZIP algorithm is implemented to compress records before saving in the central database. It is a lossless data compression utility that is based on the deflate algorithm with the format defined in Internet Engineering Task Force RFC1951: DEFLATE Compressed Data Format Specification version 1.32. This standard references the use of the LZ77 (Lempil-Ziv, 1977) compression algorithm combined with Huffman coding. On the other hand, the lossless decompressionrestores the data by bringing back the removed redundancy and produces an exact replica of the original source data. Results of the software evaluation indicate high acceptability on the overall system performance