FPGA implementation of a predictive vector quantization image compression algorithm for image sensor applications

This paper presents a hybrid image compression scheme based on a block based compression algorithm referred to as Vector Quantization (VQ) combined with the Differential Pulse Code Modulation (DPCM) technique. The proposed image compression technique called the PVQ scheme results in enhanced image quality as compared to the standalone VQ. The generated codebooks for the PVQ scheme are more robust for image coding than that of the VQ. This made our system a suitable candidate for developing on chip image sensor with integrated data compression processor. The proposed system was validated through FPGA implementation. The resulting implementation achieved good compression and image quality with moderate system complexity

Técnicas de compresión de imágenes hiperespectrales sobre hardware reconfigurable

Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 18-12-2020Sensors are nowadays in all aspects of human life. When possible, sensors are used remotely. This is less intrusive, avoids interferces in the measuring process, and more convenient for the scientist. One of the most recurrent concerns in the last decades has been sustainability of the planet, and how the changes it is facing can be monitored. Remote sensing of the earth has seen an explosion in activity, with satellites now being launched on a weekly basis to perform remote analysis of the earth, and planes surveying vast areas for closer analysis...Los sensores aparecen hoy en día en todos los aspectos de nuestra vida. Cuando es posible, de manera remota. Esto es menos intrusivo, evita interferencias en el proceso de medida, y además facilita el trabajo científico. Una de las preocupaciones recurrentes en las últimas décadas ha sido la sotenibilidad del planeta, y cómo menitoirzar los cambios a los que se enfrenta. Los estudios remotos de la tierra han visto un gran crecimiento, con satélites lanzados semanalmente para analizar la superficie, y aviones sobrevolando grades áreas para análisis más precisos...Fac. de InformáticaTRUEunpu

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

Benchmarking of Embedded Object Detection in Optical and RADAR Scenes

A portable, real-time vital sign estimation protoype is developed using neural network- based localization, multi-object tracking, and embedded processing optimizations. The system estimates heart and respiration rates of multiple subjects using directional of arrival techniques on RADAR data. This system is useful in many civilian and military applications including search and rescue. The primary contribution from this work is the implementation and benchmarking of neural networks for real time detection and localization on various systems including the testing of eight neural networks on a discrete GPU and Jetson Xavier devices. Mean average precision (mAP) and inference speed benchmarks were performed. We have shown fast and accurate detection and tracking using synthetic and real RADAR data. Another major contribution is the quantification of the relationship between neural network mAP performance and data augmentations. As an example, we focused on image and video compression methods, such as JPEG, WebP, H264, and H265. The results show WebP at a quantization level of 50 and H265 at a constant rate factor of 30 provide the best balance between compression and acceptable mAP. Other minor contributions are achieved in enhancing the functionality of the real-time prototype system. This includes the implementation and benchmarking of neural network op- timizations, such as quantization and pruning. Furthermore, an appearance-based synthetic RADAR and real RADAR datasets are developed. The latter contains simultaneous optical and RADAR data capture and cross-modal labels. Finally, multi-object tracking methods are benchmarked and a support vector machine is utilized for cross-modal association. In summary, the implementation, benchmarking, and optimization of methods for detection and tracking helped create a real-time vital sign system on a low-profile embedded device. Additionally, this work established a relationship between compression methods and different neural networks for optimal file compression and network performance. Finally, methods for RADAR and optical data collection and cross-modal association are implemented

Diseño, implementación y optimización del sistema de compresión de imágenes sobre el ordenador de a bordo del proyecto de nanosátelite Eye-Sat

Eye-Sat es un Proyecto de nano satélites, dirigido por el CNES (Centre National d’Etudes Spatiales) y desarrollado principalmente por estudiantes de varias escuelas de ingeniería del territorio francés. El objetivo de este pequeño telescopio no solo radica en la oportunidad de realizar la demostración de distintos dispositivos tecnológicos, sino que también tiene como misión la adquisición de fotografías en la bandas de color e infrarrojo de la vía Láctea, así como el estudio de la intensidad y polarización de la luz Zodiacal. Los requerimientos de la misión exigen el desarrollo de un algoritmo de compresión de imágenes sin pérdidas para las imágenes “Color Filter Array” CFA (Bayer) e infrarrojas adquiridas por el satélite. Como miembro de la comisión consultativa para los sistemas espaciales, CNES ha seleccionado el estándar CCSDS-123.0-B como algoritmo base para cumplir los requerimientos de la misión. A este algoritmo se le añadirán modificaciones o mejoras, adaptadas a las imágenes tipo, con el fin de mejorar las prestaciones de compresión y de complejidad. La implementación y la optimización del algoritmo será desarrollada sobre la plataforma Xilinx Zynq® All Programmable SoC, el cual incluye una FPGA y un Dual-core ARM® Cortex™-A9 processor with NEONTM DSP/FPU Engine

Hardware Implementation Of Conditional Motion Estimation In Video Coding

This thesis presents the rate distortion analysis of conditional motion estimation, a process in which motion computation is restricted to only active pixels in the video. We model active pixels as independent and identically distributed Gaussian process and inactive pixels as Gaussian-Markov process and derive the rate distortion function based on conditional motion estimation. Rate-Distortion curves for the conditional motion estimation scheme are also presented. In addition this thesis also presents the hardware implementation of a block based motion estimation algorithm. Block matching algorithms are difficult to implement on FPGA chip due to its complexity. We implement 2D-Logarithmic search algorithm to estimate the motion vectors for the image. The matching criterion used in the algorithm is Sum of Absolute Differences (SAD). VHDL code for the motion estimation algorithm is verified using ISim and is implemented using Xilinx ISE Design tool. Synthesis results for the algorithm are also presented

Parallel hardware architecture for JPEG-LS based on domain decomposition using context sets

This thesis investigates the scope of parallelism of the lossless JPEG-LS encoder. The input is not taken to be the entire image anymore; instead it is streams of pixels from an image sensor in every clock cycle. So the data dependencies that already exist due to the context modelling process and the effect of incomplete image data were analyzed thoroughly here. Other approaches of parallelism in JPEG-LS (e.g. pipelined hardware or software implementations that modify the context update procedures) deviate from the standard defined by ISO/ITU. On the other hand, the proposed technique here is fully compatible to the standard. In this work, a unique pixel loading mechanism (i.e. in the form that the encoder expects them to be) was developed from the streams of pixel. Later in order to store the pixels of the same context that are yet to be processed, another unique buffering mechanism was developed. However the context distribution of individual pixel determines the maximum achievable parallelism and thus a fixed value is not guaranteed in any case. The thesis also presents a vhdl implementation of the proposed parallel JPEG-LS encoder. The target hardware for this design was an FPGA board (Virtex 5). The design was also compared with the sequential hardware implementation and other parallel implementation in terms of speed up mainly. However there were some obstacles that restricted the actual synthesis. Possible reasons behind them are discussed with further suggestions for future work

Compressed sensing with continuous parametric reconstruction

This work presents a novel unconventional method of signal reconstruction after compressive sensing. Instead of usual matrices, continuous models are used to describe both the sampling process and acquired signal. Reconstruction is performed by finding suitable values of model parameters in order to obtain the most probable fit. A continuous approach allows more precise modelling of physical sampling circuitry and signal reconstruction at arbitrary sampling rate. Application of this method is demonstrated using a wireless sensor network used for freshwater quality monitoring. Results show that the proposed method is more robust and offers stable performance when the samples are noisy or otherwise distorted