Development of Low Power Image Compression Techniques

Digital camera is the main medium for digital photography. The basic operation performed by a simple digital camera is, to convert the light energy to electrical energy, then the energy is converted to digital format and a compression algorithm is used to reduce memory requirement for storing the image. This compression algorithm is frequently called for capturing and storing the images. This leads us to develop an efficient compression algorithm which will give the same result as that of the existing algorithms with low power consumption. As a result the new algorithm implemented camera can be used for capturing more images then the previous one. 1) Discrete Cosine Transform (DCT) based JPEG is an accepted standard for lossy compression of still image. Quantisation is mainly responsible for the amount loss in the image quality in the process of lossy compression. A new Energy Quantisation (EQ) method proposed for speeding up the coding and decoding procedure while preserving image qu..

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Low delay video coding

Analogue wireless cameras have been employed for decades, however they have not become an universal solution due to their difficulties of set up and use. The main problem is the link robustness which mainly depends on the requirement of a line-of-sight view between transmitter and receiver, a working condition not always possible. Despite the use of tracking antenna system such as the Portable Intelligent Tracking Antenna (PITA [1]), if strong multipath fading occurs (e.g. obstacles between transmitter and receiver) the picture rapidly falls apart. Digital wireless cameras based on Orthogonal Frequency Division Multiplexing (OFDM) modulation schemes give a valid solution for the above problem. OFDM offers strong multipath protection due to the insertion of the guard interval; in particular, the OFDM-based DVB-T standard has proven to offer excellent performance for the broadcasting of multimedia streams with bit rates over 10 Mbps in difficult terrestrial propagation channels, for fixed and portable applications. However, in typical conditions, the latency needed to compress/decompress a digital video signal at Standard Definition (SD) resolution is of the order of 15 frames, which corresponds to ≃ 0.5 sec. This delay introduces a serious problem when wireless and wired cameras have to be interfaced. Cabled cameras do not use compression, because the cable which directly links transmitter and receiver does not impose restrictive bandwidth constraints. Therefore, the only latency that affects a cable cameras link system is the on cable propagation delay, almost not significant, when switching between wired and wireless cameras, the residual latency makes it impossible to achieve the audio-video synchronization, with consequent disagreeable effects. A way to solve this problem is to provide a low delay digital processing scheme based on a video coding algorithm which avoids massive intermediate data storage. The analysis of the last MPEG based coding standards puts in evidence a series of problems which limits the real performance of a low delay MPEG coding system. The first effort of this work is to study the MPEG standard to understand its limit from both the coding delay and implementation complexity points of views. This thesis also investigates an alternative solution based on HERMES codec, a proprietary algorithm which is described implemented and evaluated. HERMES achieves better results than MPEG in terms of latency and implementation complexity, at the price of higher compression ratios, which means high output bit rates. The use of HERMES codec together with an enhanced OFDM system [2] leads to a competitive solution for wireless digital professional video applications

An Iterative Soft Decision Based LR-Aided MIMO Detector

The demand for wireless and high-rate communication system is increasing gradually and multiple-input-multiple-output (MIMO) is one of the feasible solutions to accommodate the growing demand for its spatial multiplexing and diversity gain. However, with high number of antennas, the computational and hardware complexity of MIMO increases exponentially. This accumulating complexity is a paramount problem in MIMO detection system directly leading to large power consumption. Hence, the major focus of this dissertation is algorithmic and hardware development of MIMO decoder with reduced complexity for both real and complex domain, which can be a beneficial solution with power efficiency and high throughput. Both hard and soft domain MIMO detectors are considered. The use of lattice reduction (LR) algorithm and on-demand-child-expansion for the reduction of noise propagation and node calculation respectively are the two of the key features of our developed architecture, presented in this literature. The real domain iterative soft MIMO decoding algorithm, simulated for 4 × 4 MIMO with different modulation scheme, achieves 1.1 to 2.7 dB improvement over Lease Sphere Decoder (LSD) and more than 8x reduction in list size, K as well as complexity of the detector. Next, the iterative real domain K-Best decoder is expanded to the complex domain with new detection scheme. It attains 6.9 to 8.0 dB improvement over real domain K-Best decoder and 1.4 to 2.5 dB better performance over conventional complex decoder for 8 × 8 MIMO with 64 QAM modulation scheme. Besides K, a new adjustable parameter, Rlimit has been introduced in order to append re-configurability trading-off between complexity and performance. After that, a novel low-power hardware architecture of complex decoder is developed for 8 × 8 MIMO and 64 QAM modulation scheme. The total word length of only 16 bits has been adopted limiting the bit error rate (BER) degradation to 0.3 dB with K and Rlimit equal to 4. The proposed VLSI architecture is modeled in Verilog HDL using Xilinx and synthesized using Synopsys Design Vision in 45 nm CMOS technology. According to the synthesize result, it achieves 1090.8 Mbps throughput with power consumption of 580 mW and latency of 0.33 us. The maximum frequency the design proposed is 181.8 MHz. All of the proposed decoders mentioned above are bounded by the fixed K. Hence, an adaptive real domain K-Best decoder is further developed to achieve the similar performance with less K, thereby reducing the computational complexity of the decoder. It does not require accurate SNR measurement to perform the initial estimation of list size, K. Instead, the difference between the first two minimal distances is considered, which inherently eliminates complexity. In summary, a novel iterative K-Best detector for both real and complex domain with efficient VLSI design is proposed in this dissertation. The results from extensive simulation and VHDL with analysis using Synopsys tool are also presented for justification and validation of the proposed works

An Iterative Soft Decision Based LR-Aided MIMO Detector

The demand for wireless and high-rate communication system is increasing gradually and multiple-input-multiple-output (MIMO) is one of the feasible solutions to accommodate the growing demand for its spatial multiplexing and diversity gain. However, with high number of antennas, the computational and hardware complexity of MIMO increases exponentially. This accumulating complexity is a paramount problem in MIMO detection system directly leading to large power consumption. Hence, the major focus of this dissertation is algorithmic and hardware development of MIMO decoder with reduced complexity for both real and complex domain, which can be a beneficial solution with power efficiency and high throughput. Both hard and soft domain MIMO detectors are considered. The use of lattice reduction (LR) algorithm and on-demand-child-expansion for the reduction of noise propagation and node calculation respectively are the two of the key features of our developed architecture, presented in this literature. The real domain iterative soft MIMO decoding algorithm, simulated for 4 × 4 MIMO with different modulation scheme, achieves 1.1 to 2.7 dB improvement over Lease Sphere Decoder (LSD) and more than 8x reduction in list size, K as well as complexity of the detector. Next, the iterative real domain K-Best decoder is expanded to the complex domain with new detection scheme. It attains 6.9 to 8.0 dB improvement over real domain K-Best decoder and 1.4 to 2.5 dB better performance over conventional complex decoder for 8 × 8 MIMO with 64 QAM modulation scheme. Besides K, a new adjustable parameter, Rlimit has been introduced in order to append re-configurability trading-off between complexity and performance. After that, a novel low-power hardware architecture of complex decoder is developed for 8 × 8 MIMO and 64 QAM modulation scheme. The total word length of only 16 bits has been adopted limiting the bit error rate (BER) degradation to 0.3 dB with K and Rlimit equal to 4. The proposed VLSI architecture is modeled in Verilog HDL using Xilinx and synthesized using Synopsys Design Vision in 45 nm CMOS technology. According to the synthesize result, it achieves 1090.8 Mbps throughput with power consumption of 580 mW and latency of 0.33 us. The maximum frequency the design proposed is 181.8 MHz. All of the proposed decoders mentioned above are bounded by the fixed K. Hence, an adaptive real domain K-Best decoder is further developed to achieve the similar performance with less K, thereby reducing the computational complexity of the decoder. It does not require accurate SNR measurement to perform the initial estimation of list size, K. Instead, the difference between the first two minimal distances is considered, which inherently eliminates complexity. In summary, a novel iterative K-Best detector for both real and complex domain with efficient VLSI design is proposed in this dissertation. The results from extensive simulation and VHDL with analysis using Synopsys tool are also presented for justification and validation of the proposed works

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin