Exclusive-or preprocessing and dictionary coding of continuous-tone images.

The field of lossless image compression studies the various ways to represent image data in the most compact and efficient manner possible that also allows the image to be reproduced without any loss. One of the most efficient strategies used in lossless compression is to introduce entropy reduction through decorrelation. This study focuses on using the exclusive-or logic operator in a decorrelation filter as the preprocessing phase of lossless image compression of continuous-tone images. The exclusive-or logic operator is simply and reversibly applied to continuous-tone images for the purpose of extracting differences between neighboring pixels. Implementation of the exclusive-or operator also does not introduce data expansion. Traditional as well as innovative prediction methods are included for the creation of inputs for the exclusive-or logic based decorrelation filter. The results of the filter are then encoded by a variation of the Lempel-Ziv-Welch dictionary coder. Dictionary coding is selected for the coding phase of the algorithm because it does not require the storage of code tables or probabilities and because it is lower in complexity than other popular options such as Huffman or Arithmetic coding. The first modification of the Lempel-Ziv-Welch dictionary coder is that image data can be read in a sequence that is linear, 2-dimensional, or an adaptive combination of both. The second modification of the dictionary coder is that the coder can instead include multiple, dynamically chosen dictionaries. Experiments indicate that the exclusive-or operator based decorrelation filter when combined with a modified Lempel-Ziv-Welch dictionary coder provides compression comparable to algorithms that represent the current standard in lossless compression. The proposed algorithm provides compression performance that is below the Context-Based, Adaptive, Lossless Image Compression (CALIC) algorithm by 23%, below the Low Complexity Lossless Compression for Images (LOCO-I) algorithm by 19%, and below the Portable Network Graphics implementation of the Deflate algorithm by 7%, but above the Zip implementation of the Deflate algorithm by 24%. The proposed algorithm uses the exclusive-or operator in the modeling phase and uses modified Lempel-Ziv-Welch dictionary coding in the coding phase to form a low complexity, reversible, and dynamic method of lossless image compression

EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators

In the wake of the success of convolutional neural networks in image classification, object recognition, speech recognition, etc., the demand for deploying these compute-intensive ML models on embedded and mobile systems with tight power and energy constraints at low cost, as well as for boosting throughput in data centers, is growing rapidly. This has sparked a surge of research into specialized hardware accelerators. Their performance is typically limited by I/O bandwidth, power consumption is dominated by I/O transfers to off-chip memory, and on-chip memories occupy a large part of the silicon area. We introduce and evaluate a novel, hardware-friendly, and lossless compression scheme for the feature maps present within convolutional neural networks. We present hardware architectures and synthesis results for the compressor and decompressor in 65 nm. With a throughput of one 8-bit word/cycle at 600 MHz, they fit into 2.8 kGE and 3.0 kGE of silicon area, respectively - together the size of less than seven 8-bit multiply-add units at the same throughput. We show that an average compression ratio of 5.1 7 for AlexNet, 4 for VGG-16, 2.4 7 for ResNet-34 and 2.2 7 for MobileNetV2 can be achieved - a gain of 45-70% over existing methods. Our approach also works effectively for various number formats, has a low frame-to-frame variance on the compression ratio, and achieves compression factors for gradient map compression during training that are even better than for inference

GaAs Implementation of FIR Filter

This thesis discusses the findings of the final year project involving Gallium Arsenide implementation of a triangular FIR filter to perform discrete wavelet transforms. The overall characteristics of Gallium Arsenide technology- its construction, behaviour and electrical charactersitics as they apply to VLSI technology - were investigated in this project. In depth understanding of its architecture is required to be able to understand the various design techniques employed. A comparison of Silicon and GaAs performance and other characteristics has also been made to fully justify the choice of this material for system implementation. A lot of research and active interest has gone into the field of image and video compression. Wavelet-based image transformation is one of the very efficient compression techniques used. An analysis of discrete wavelet transformations and the required triangular FIR filter was done to be able to produce a transform algorithm and the related filter architecture. Finally, the filter architecture was implemented as a VLSI design and layout. A variety of functional blocks required for the architecture were designed, tested and analysed. All these blocks were integrated to produce a model of a complete filter cell. The filter implementation was designed to be self-timed - without a system clock. Self-timed systems have considerable advantages over clocked architectures. Various design styles and handshaking mechanisms involved in designing a self-timed system were analysed and designed. There are many avenues still to explore. One of them is the VHDL analysis of filter architecture. Further development on this project would involve integration of higher-level logic and formation of a complete filter array

Comparação entre os algoritmos de codificação Huffman e Lempel-Ziv para compressão de sinais de áudio após a filtragem

The Huffman and Lempel-Ziv coding algorithms are extensively used in digital communications for data compression. Through this data compression it is possible to significantly decrease the file size, reducing data storage costs and making the systems faster and more efficient. In this work, it is studied which of these algorithms has the best compression performance for filtered and unfiltered audio signals. The metrics used to analyze each performance are: signal-to-noise ratio, average code length and compression ratio. Moreover, Matlab software is used to simulate the distinct scenarios presented here.Trabalho de Conclusão de Curso (Graduação)Os algoritmos de codificação Huffman e Lempel-Ziv são extensivamente utilizados em comunicações digitais para a compressão de dados. Por meio dessa compressão é possível reduzir significativamente o tamanho dos arquivos, possibilitando a redução de gastos com armazenamento de dados e tornando os sistemas mais rápidos e eficientes. Neste trabalho é estudado qual desses algoritmos tem o melhor desempenho de compressão para sinais de áudio filtrados e não filtrados. As métricas utilizadas para a análise de cada desempenho são: relação sinal ruído, comprimento médio de código e taxa de compressão. Em função disso, é utilizado o software Matlab para a simulação dos diferentes panoramas aqui apresentados

Demonstration of Inexact Computing Implemented in the JPEG Compression Algorithm using Probabilistic Boolean Logic applied to CMOS Components

Probabilistic computing offers potential improvements in energy, performance, and area compared with traditional digital design. This dissertation quantifies energy and energy-delay tradeoffs in digital adders, multipliers, and the JPEG image compression algorithm. This research shows that energy demand can be cut in half with noisesusceptible16-bit Kogge-Stone adders that deviate from the correct value by an average of 3 in 14 nanometer CMOS FinFET technology, while the energy-delay product (EDP) is reduced by 38 . This is achieved by reducing the power supply voltage which drives the noisy transistors. If a 19 average error is allowed, the adders are 13 times more energy-efficient and the EDP is reduced by 35 . This research demonstrates that 92 of the color space transform and discrete cosine transform circuits within the JPEG algorithm can be built from inexact components, and still produce readable images. Given the case in which each binary logic gate has a 1 error probability, the color space transformation has an average pixel error of 5.4 and a 55 energy reduction compared to the error-free circuit, and the discrete cosine transformation has a 55 energy reduction with an average pixel error of 20

Efficient simultaneous encryption and compression of digital videos in computationally constrained applications

This thesis is concerned with the secure video transmission over open and wireless network channels. This would facilitate adequate interaction in computationally constrained applications among trusted entities such as in disaster/conflict zones, secure airborne transmission of videos for intelligence/security or surveillance purposes, and secure video communication for law enforcing agencies in crime fighting or in proactive forensics. Video content is generally too large and vulnerable to eavesdropping when transmitted over open network channels so that compression and encryption become very essential for storage and/or transmission. In terms of security, wireless channels, are more vulnerable than other kinds of mediums to a variety of attacks and eavesdropping. Since wireless communication is the main mode in the above applications, protecting video transmissions from unauthorized access through such network channels is a must. The main and multi-faceted challenges that one faces in implementing such a task are related to competing, and to some extent conflicting, requirements of a number of standard control factors relating to the constrained bandwidth, reasonably high image quality at the receiving end, the execution time, and robustness against security attacks. Applying both compression and encryption techniques simultaneously is a very tough challenge due to the fact that we need to optimize the compression ratio, time complexity, security and the quality simultaneously. There are different available image/video compression schemes that provide reasonable compression while attempting to maintain image quality, such as JPEG, MPEG and JPEG2000. The main approach to video compression is based on detecting and removing spatial correlation within the video frames as well as temporal correlations across the video frames. Temporal correlations are expected to be more evident across sequences of frames captured within a short period of time (often a fraction of a second). Correlation can be measured in terms of similarity between blocks of pixels. Frequency domain transforms such as the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT) have both been used restructure the frequency content (coefficients) to become amenable for efficient detection. JPEG and MPEG use DCT while JPEG2000 uses DWT. Removing spatial/temporal correlation encodes only one block from each class of equivalent (i.e. similar) blocks and remembering the position of all other block within the equivalence class. JPEG2000 compressed images achieve higher image quality than JPEG for the same compression ratios, while DCT based coding suffer from noticeable distortion at high compression ratio but when applied to any block it is easy to isolate the significant coefficients from the non-significant ones. Efficient video encryption in computationally constrained applications is another challenge on its own. It has long been recognised that selective encryption is the only viable approach to deal with the overwhelming file size. Selection can be made in the spatial or frequency domain. Efficiency of simultaneous compression and encryption is a good reason for us to apply selective encryption in the frequency domain. In this thesis we develop a hybrid of DWT and DCT for improved image/video compression in terms of image quality, compression ratio, bandwidth, and efficiency. We shall also investigate other techniques that have similar properties to the DCT in terms of representation of significant wavelet coefficients. The statistical properties of wavelet transform high frequency sub-bands provide one such approach, and we also propose phase sensing as another alternative but very efficient scheme. Simultaneous compression and encryption, in our investigations, were aimed at finding the best way of applying these two tasks in parallel by selecting some wavelet sub-bands for encryptions and applying compression on the other sub-bands. Since most spatial/temporal correlation appear in the high frequency wavelet sub-bands and the LL sub-bands of wavelet transformed images approximate the original images then we select the LL-sub-band data for encryption and the non-LL high frequency sub-band coefficients for compression. We also follow the common practice of using stream ciphers to meet efficiency requirements of real-time transmission. For key stream generation we investigated a number of schemes and the ultimate choice will depend on robustness to attacks. The still image (i.e. RF’s) are compressed with a modified EZW wavelet scheme by applying the DCT on the blocks of the wavelet sub-bands, selecting appropriate thresholds for determining significance of coefficients, and encrypting the EZW thresholds only with a simple 10-bit LFSR cipher This scheme is reasonably efficient in terms of processing time, compression ratio, image quality, as well was security robustness against statistical and frequency attack. However, many areas for improvements were identified as necessary to achieve the objectives of the thesis. Through a process of refinement we developed and tested 3 different secure efficient video compression schemes, whereby at each step we improve the performance of the scheme in the previous step. Extensive experiments are conducted to test performance of the new scheme, at each refined stage, in terms of efficiency, compression ratio, image quality, and security robustness. Depending on the aspects of compression that needs improvement at each refinement step, we replaced the previous block coding scheme with a more appropriate one from among the 3 above mentioned schemes (i.e. DCT, Edge sensing and phase sensing) for the reference frames or the non-reference ones. In subsequent refinement steps we apply encryption to a slightly expanded LL-sub-band using successively more secure stream ciphers, but with different approaches to key stream generation. In the first refinement step, encryption utilized two LFSRs seeded with three secret keys to scramble the significant wavelet LL-coefficients multiple times. In the second approach, the encryption algorithm utilises LFSR to scramble the wavelet coefficients of the edges extracted from the low frequency sub-band. These edges are mapped from the high frequency sub-bands using different threshold. Finally, use a version of the A5 cipher combined with chaotic logistic map to encrypt the significant parameters of the LL sub-band. Our empirical results show that the refinement process achieves the ultimate objectives of the thesis, i.e. efficient secure video compression scheme that is scalable in terms of the frame size at about 100 fps and satisfying the following features; high compression, reasonable quality, and resistance to the statistical, frequency and the brute force attack with low computational processing. Although image quality fluctuates depending on video complexity, in the conclusion we recommend an adaptive implementation of our scheme. Although this thesis does not deal with transmission tasks but the efficiency achieved in terms of video encryption and compression time as well as in compression ratios will be sufficient for real-time secure transmission of video using commercially available mobile computing devices

Performance Analysis of Hybrid Algorithms For Lossless Compression of Climate Data

Climate data is very important and at the same time, voluminous. Every minute a new entry is recorded for different climate parameters in climate databases around the world. Given the explosive growth of data that needs to be transmitted and stored, there is a necessity to focus on developing better transmission and storage technologies. Data compression is known to be a viable and effective solution to reduce bandwidth and storage requirements of bulk data. So, the goal is to develop the best compression methods for climate data. The methodology used is based on predictive analysis. The focus is to implement a hybrid algorithm which utilizes the functionality of Artificial Neural Networks (ANN) for prediction of climate data. ANN is a very efficient tool to generate models for predicting climate data with great accuracy. Two types of ANN’s such as Multilayer Perceptron (MLP) and Cascade Feedforward Neural Network (CFNN) are used. It is beneficial to take advantage of ANN and combine its output with lossless compression algorithms such as differential encoding and Huffman coding to generate high compression ratios. The performance of the two techniques based on MLP and CFNN types are compared using metrics including compression ratio, Mean Square Error (MSE) and Root Mean Square Error (RMSE). The two methods are also compared with a conventional method of differential encoding followed by Huffman Coding. The results indicate that MLP outperforms CFNN. Also compression ratios of both the proposed methods are higher than those obtained by the standard method. Compression ratios as high as 10.3, 9.8, and 9.54 are obtained for precipitation, photosynthetically active radiation, and solar radiation datasets respectively