Algorithms for compression of high dynamic range images and video

The recent advances in sensor and display technologies have brought upon the High Dynamic Range (HDR) imaging capability. The modern multiple exposure HDR sensors can achieve the dynamic range of 100-120 dB and LED and OLED display devices have contrast ratios of 10^5:1 to 10^6:1. Despite the above advances in technology the image/video compression algorithms and associated hardware are yet based on Standard Dynamic Range (SDR) technology, i.e. they operate within an effective dynamic range of up to 70 dB for 8 bit gamma corrected images. Further the existing infrastructure for content distribution is also designed for SDR, which creates interoperability problems with true HDR capture and display equipment. The current solutions for the above problem include tone mapping the HDR content to fit SDR. However this approach leads to image quality associated problems, when strong dynamic range compression is applied. Even though some HDR-only solutions have been proposed in literature, they are not interoperable with current SDR infrastructure and are thus typically used in closed systems. Given the above observations a research gap was identified in the need for efficient algorithms for the compression of still images and video, which are capable of storing full dynamic range and colour gamut of HDR images and at the same time backward compatible with existing SDR infrastructure. To improve the usability of SDR content it is vital that any such algorithms should accommodate different tone mapping operators, including those that are spatially non-uniform. In the course of the research presented in this thesis a novel two layer CODEC architecture is introduced for both HDR image and video coding. Further a universal and computationally efficient approximation of the tone mapping operator is developed and presented. It is shown that the use of perceptually uniform colourspaces for internal representation of pixel data enables improved compression efficiency of the algorithms. Further proposed novel approaches to the compression of metadata for the tone mapping operator is shown to improve compression performance for low bitrate video content. Multiple compression algorithms are designed, implemented and compared and quality-complexity trade-offs are identified. Finally practical aspects of implementing the developed algorithms are explored by automating the design space exploration flow and integrating the high level systems design framework with domain specific tools for synthesis and simulation of multiprocessor systems. The directions for further work are also presented

VLSI architecture design approaches for real-time video processing

This paper discusses the programmable and dedicated approaches for real-time video processing applications. Various VLSI architecture including the design examples of both approaches are reviewed. Finally, discussions of several practical designs in real-time video processing applications are then considered in VLSI architectures to provide significant guidelines to VLSI designers for any further real-time video processing design works

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

Energy Efficiency of Image Transmission in Embedded Linux based Wireless Visual Sensor Network

Wireless Visual Sensor Network (WVSN) is a system that consists of visual sensor nodes with an embedded processor. WVSN devices have limited resources of energy, computation capability, memory, and bandwidth. Due to these limitations the implementation of WVSN for large multimedia data, such as images, become a challenging task. Therefore, it is required compressed images prior to transmission. In addition to the limited resources, the system implementation strongly affects the efficiency of the working system. The main contribution of this research is to offer a technical solution of simpler image compression on the WVSN platform. JPEG 2000 is investigated as an alternative compression method to reduce the size of data transfer on WVSN using Embedded Linux as its operating system. Compressed images are transferred to a receiver on communication of IEEE 802.15.4.. This paper shows that the energy consumption for compression and transmission will reduce to only 10.48%, 13.60%, and 17.11% compared to raw image. BER will significantly reduce by implementing image compression. Therefore, it is demonstrated that this model significantly increases energy efficiency, memory utilization efficiency, and data transfer time with acceptable PSNR, compared to uncompressed images

Memory-efficient lossless video compression using temporal extended JPEG-LS and on-line compression

Use of temporal predictors in lossless video coders play a significant role in terms of compression gain, but comes with a cost of significant memory requirement since this approach requires to save at least one frame in buffer for residue calculation. An improvement to standard JPEG-LS based lossless video coding algorithm is proposed in this work which requires very small amount of memory comparing to the regular approach keeping the computational complexity low. To obtain a higher compression, a combination of spatial and temporal predictor model has been used where appropriate mode is selected adaptively on a pixel based analysis. Using only one reference frame, the context based temporal coder performs its calculation regarding mode selection and prediction error calculation with already reconstructed pixels. This method eliminates the overhead of transmitting the coding mode in the decoder side. The need for storage space to save the only reference frame is further reduced by introducing on-line lossy compression on that frame. Relevant pixels from the stored reference frame are obtained by partial on-the-fly decompression. The combination of temporally extended context based prediction and on-line compression achieves a significant gain in compression ratio comparing to standard frame-by-frame JPEG-LS video coding keeping the memory requirement low, making it usable as a lightweight lossless video coder for embedded systems