An Improved Implementation Scheme for AVS Entropy Decoding Based on FPGA

Multipath diversity leads to a possible higher performance for real-time high definition video, especially for medical video transmission, which would improve the stability of multiple transmission paths in the symmetrical state, and avoid the potential losses of imaging information in the communication process. Most of the previous works are always based on the single-path end-to-end transmission, although the service had been demonstrated that it is unable to meet the rigorous demand for the RT-HDMV. In the paper, a multipath relay service based on the QoE (quality of experience) evaluation method is proposed for the RT-HDMV (real-time high definition medical video). The method eliminates several of the limitations in the existing methods for some conventional single-path transmission. It can fully utilize the finite network resources and transmission bandwidth to meet the users’ demands of the RT-HDMV to get a better score of the QoE. We use a four-stage framework to evaluate the QoE, which consists of constructing the multipath relay transmission for the RT-HDMV, calculating the weights of diversified QoS parameters in the multipath, designing the load distribution strategy by the mapping between the QoS (quality of service) and QoE, and redefining the rule of the QoE evaluation. Many experiments show that the proposed design scheme achieves weighting of the transmission sub-paths and computes the QoE score. Compared with the state-of-art methods in the single path transmission scene, our framework mainly gains the excellent performance for the RT-HDMV.

Interpolation is the main bottleneck in AVS real-time high definition video encoder for its high memory bandwidth and large calculation complexity caused by the new coding features of variable block size and 4-tap filter. In this paper, an efficient VLSI architecture of interpolation supporting AVS Baseline@L4 is presented. Vertical redundant data reuse, horizontal redundant data reuse and sub-pixel data reuse schemes are presented to reduce memory bandwidth and processing cycle. The separated 1-D interpolation filters are used to improve throughput and hardware utilization. The proposed design is implemented on the Vertex4 XC4VLX200 field programmable gate array with operating frequency of 150 MHz and can support 1080p (1920 times 1080) 30 fps AVS real-time encoder. It is a useful intellectual property design for real-time high definition video application.

Fast and accurate salient-object detectors are important for various image processing and computer vision applications, such as adaptive compression and object segmentation. It is also desirable to have a detector that is aware of the position and the size of the salient objects. In this paper, we propose a salient-object detection method that is fast, accurate, and size-aware. For efficient computation, we quantize the image colors and estimate the spatial positions and sizes of the quantized colors. We then feed these values into a statistical model to obtain a probability of saliency. In order to estimate the final saliency, this probability is combined with a global color contrast measure. We test our method on two public datasets and show that our method significantly outperforms the fast state-of-the-art methods. In addition, it has comparable performance and is an order of magnitude faster than the accurate state-of-the-art methods. We exhibit the potential of our algorithm by processing a high-definition video in real time.

Since the 1980s, the development of information and communication technologies (ICTs) has greatly changed people’s modes of production and lifestyle, and it has also had a significant influence on traditional social structures. Microblogs – a type of social media application such as Twitter or Weibo – have served as an important platform for network governance in some local governments in China. This study makes an attempt to answer the following questions: What types of strategies should governments implement on social media platforms during public emergencies? What are the effects of these strategies? Based on the case of the Shifang Incident, which was a large-scale environmental protest that occurred in Shifang, China in 2012, we analyze all the messages posted during the incident on the official microblog of the Shifang government and examine the public feedback by using an online big data analysis tool. In line with the time sequence and the extent of the conflict, we divide the Shifang Incident into three phases: the fermentation period, the confrontation period, and the digestion period. In addition, we classify government strategies on social media into five categories: introducing, appealing, explaining, rumor-refuting, and decision-making. The analysis results show that different government strategies are applied to different phases of the incident and that the responses of the public also vary during different periods.

A Water Resources Planning Response to Climate Change in the Senegal River Basin

Fixed underwater observatories (FUO), equipped with digital cameras and other sensors, become more commonly used to record different kinds of time series data for marine habitat monitoring. With increasing numbers of campaigns, numbers of sensors and campaign time, the volume and heterogeneity of the data, ranging from simple temperature time series to series of HD images or video call for new data science approaches to analyze the data. While some works have been published on the analysis of data from one campaign, we address the problem of analyzing time series data from two consecutive monitoring campaigns (starting late 2017 and late 2018) in the same habitat. While the data from campaigns in two separate years provide an interesting basis for marine biology research, it also presents new data science challenges, like the the marine image analysis in data form more than one campaign. In this paper, we analyze the polyp activity of two Paragorgia arborea cold water coral (CWC) colonies using FUO data collected from November 2017 to June 2018 and from December 2018 to April 2019. We successfully apply convolutional neural networks (CNN) for the segmentation and classification of the coral and the polyp activities. The result polyp activity data alone showed interesting temporal patterns with differences and similarities between the two time periods. A one month "sleeping" period in spring with almost no activity was observed in both coral colonies, but with a shift of approximately one month. A time series prediction experiment allowed us to predict the polyp activity from the non-image sensor data using recurrent neural networks (RNN). The results pave a way to a new multi-sensor monitoring strategy for Paragorgia arborea behaviour.

This paper uses joint algorithm and architecture design to enable high coding efficiency in conjunction with high processing speed and low area cost. Specifically, it presents several optimizations that can be performed on Context Adaptive Binary Arithmetic Coding (CABAC), a form of entropy coding used in H.264/AVC, to achieve the throughput necessary for real-time low power high definition video coding. The combination of syntax element partitions and interleaved entropy slices, referred to as Massively Parallel CABAC, increases the number of binary symbols that can be processed in a cycle. Subinterval reordering is used to reduce the cycle time required to process each binary symbol. Under common conditions using the JM12.0 software, the Massively Parallel CABAC, increases the bins per cycle by 2.7 to 32.8× at a cost of 0.25 to 6.84% coding loss compared with sequential single slice H.264/AVC CABAC. It also provides a 2× reduction in area cost, and reduces memory bandwidth. Subinterval reordering reduces the critical path delay by 14 to 22%, while modifications to context selection reduces the memory requirement by 67%. This work demonstrates that accounting for implementation cost during video coding algorithms design can enable higher processing speed and reduce hardware cost, while still delivering high coding efficiency in the next generation video coding standard.

Limited computing resources in portable multimedia devices are an obstacle in real-time video decoding of high resolution and/or high quality video contents. Ordinary H.264/AVC video decoders cannot decode video contents that exceed the limits set by their processing resources. However, in many real applications especially on portable devices, a simplified decoding with some acceptable degradation may be desirable instead of just refusing to decode such contents. For this purpose, a complexity-scalable H.264/AVC video decoding scheme is investigated in this paper. First, several simplified methods of decoding tools that have different characteristics are investigated to reduce decoding complexity and consequential degradation of reconstructed video. Then a complexity scalable H.264/AVC decoding scheme is designed by selectively combining effective simplified methods to achieve the minimum degradation. Experimental results with the H.264/AVC main profile bitstream show that its decoding complexity can be scalably controlled, and reduced by up to 44% without subjective quality loss.

<p>After World War II, aerial photography i.e. vertical or oblique high-resolution aerial images spread rapidly into civil research sectors, such as landscape studies, geologic maps, natural sciences, archaeology, and more. Applying photogrammetric techniques, two or more overlapping historical aerial images can be used to generate an orthophoto and a 3D point cloud, wherefrom a digital elevation model can be derived for the respective epoch. Combining results from different epochs, morphological processes and elevation changes of the surface caused by anthropogenic and natural factors can be assessed. Despite the unequalled potential of such data, their use is not yet fully exploited. Indeed, there is a lack of clear processing workflows applying either traditional photogrammetric techniques or structure from motion (SfM) with camera self-calibration. In fact, on the one hand, many SfM and multi-view stereo software do not deal with scanned images. On the other hand, traditional photogrammetric approaches require information such as a camera calibration protocol with fiducial mark positions. Furthermore, the quality of the generated products is strongly affected by the quality of the scanned images, in terms of the conservation of the original film, scanner resolution, and acquisition parameters like image overlap and flying height.</p><p>To process a large dataset of historical images, an approach based on multi-epoch bundle adjustment has been suggested recently.  The idea is to jointly orient the images of all epochs of a historical image dataset. This recent approach relies on the robustness of the scale-invariant feature transform (SIFT) algorithm to automatically detect common features between images of the time series located in stable areas. However, this approach cannot be applied to process digital images of alpine environments, characterized by continuous changes also of small magnitude that might be challenging to automatically identify in image space. In this respect, our method implemented in OrientAL, a software developed by TU Wien, identifies stable areas in object space across the entire time series. After the joint orientation of the multi-epoch aerial images, dense image matching is performed independently for each epoch. We tested our method on an image block over the alpine catchment Kaunertal (Austria), captured at nine different epochs with a time span of fifty years. Our method definitely speeds up the process of image orientation of the entire data set, since stable areas do not need to be masked manually in each image. Furthermore, we could improve the orientation of images from epochs with poor overlap. To estimate the improvements obtained with our methods in terms of time and accuracy of the image orientation, we compare our results with photogrammetric and commercial SfM software and we analyse the accuracy of tie points with respect to a reference Lidar point cloud. The work is part of the SEHAG project (project number I 4062) funded by the DFG and FWF.</p>

Rendering performance of consumer graphics hardware benefits from pre-processing geometric data into a form targeted to the underlying API and hardware. The various elements of geometric data are then coupled with a shading program at runtime to draw the asset.In this paper we describe a system in which pre-processing is done in a compilation process in which the geometric data are processed with knowledge of their shading programs. The data are converted into structures targeted directly to the hardware, and a code stream is assembled that describes the manipulations required to render these data structures. Our compiler is structured like a traditional code compiler, with a front end that reads the geometric data and attributes (hereafter referred to as an art asset) output from a 3D modeling package and shaders in a platform independent form and performs platform-independent optimizations, and a back end that performs platform-specific optimizations and generates platform-targeted data structures and code streams.Our compiler back-end has been targeted to four platforms, three of which are radically different from one another. On all platforms the rendering performance of our compiled assets, used in real situations, is well above that of hand-coded assets.

An accurate prediction of complex product quality parameters from process time series by an end-to-end learning approach remains a significant challenge in machine learning. A special difficulty is the application of industrial batch process data because many batch processes generate variable length time series. In the industrial application of such methods, explainability is often desired. In this study, a 1D convolutional neural network (CNN) algorithm with a masking layer is proposed to solve the problem for time series of variable length. In addition, a novel combination of 1D CNN and class activation mapping (CAM) technique is part of this study to better understand the model results and highlight some regions of interest in the time series. As a comparative state-of-the-art unsupervised machine learning method, the One-Nearest Neighbours (1NN) algorithm combined with dynamic time warping (DTW) was used. Both methods are investigated as end-to-end learning methods with balanced and unbalanced class distributions and with scaled and unscaled input data, respectively. The FastDTW and DTAIDistance algorithms were investigated for the DTW calculation. The data set is made up of sensor signals that was collected during the production of plastic parts. The objective was to predict a quality parameter of plastic parts during production. For this research, the quality parameter will be a difficult or only destructively measurable parameter and both methods will be investigated for their applicability to this prediction task. The application of the proposed approach to an industrial facility for producing plastic products shows a prediction accuracy of 83.7%. It can improve the reverence method by approximately 1.4%. In addition to the slight increase in accuracy, the CNN training time was significantly reduced compared to the DTW calculation.

In this paper, we address the problem of the decoding of variable length physical layer header (PLH) codes. We take as a case study the DVB-S2X standard, and, after selecting a suitable PLH code with variable codeword length, we propose two alternative noncoherent decoding strategies. The proposed decoding strategies allow to estimate the length of the transmitted PLH field, which is unknown at the receiver, jointly with the decoding of the PLH code. We demonstrate that it is possible to achieve an excellent decoding performance while significantly reducing the overhead due to the transmission of the PLH field with respect to a standard fixed-length PLH.

In order to realize the long-distance and low-loss transmission of vehicle high-definition video, the framework and principle of vehicle camera transmission system are studied in this paper. By combining the hardware circuit of the vehicle camera transmission system, hardware coding and decoding and the principle of video signal coding and transmission, a vehicle forward-looking camera module is developed in this paper. First of all, according to the research results, the overall environmental framework of the transmission system is simulated and built. Then, the research conclusions of the power supply and the initial coding and decoding of the video signal are applied in practice, so that it can encode stably and transmit the video effectively. Then, this article further explored the architecture of the serializer/deserializer, complete the configuration of the hardware, at the same time, collect and summarize the data waveforms. Finally, this paper analyzes and evaluates the image quality obtained after restoration, and completes a research on the vehicle-mounted camera signal transmission system scheme with serializer and deserializer as the core. The experimental results show that stable and real-time high-definition video images can be obtained by using serializer and deserializer chip and using differential signal transmission protocol with interface type FPD-Link III LVDS to transmit video signals over a long distance. The application of this scheme can basically transmit 1280×720 video images with long distance and lossless resolution, and finally serve for the current intelligent driving of cars.

In this paper we present several solutions to Security Surveillance and their applications while utilizing parallel processing for improved performance. The algorithms presented in this paper are explained in detail and their implementations are provided for free for educational purposes. These algorithms with their implementations are being taught in our Parallel Processing course utilizing NVidia's Cuda language and framework. We chose the topic of Security Surveillance in our Parallel Processing course because the results are visual and applicable in many situations such as Surveillance of parking lots (monitoring our car for example), dormitory rooms, offices, etc. Image processing techniques and algorithms are discussed in this paper, and extended to real-time high definition video processing focusing on surveillance.

This paper describes the architecture, functionality, and design of HDVO-a high definition video co-processor from Philips Semiconductors. The coprocessor design is modular and functionally complete, and is intended to be a part of and function as the primary picture/video composition hardware in any media-processor chip targeted at Digital Television (DTV) applications. Essentially a screen-refresh engine, HDVO can mix multiple video and graphics planes and is capable of scaling vertically and horizontally pictures of the highest resolution (1920/spl times/1080) specified in the DTV standard prescribed by the United States Advanced Television Systems Committee (ATSC). HDVO can efficiently process all (eighteen) ATSC video formats, including video generation for picture-in-picture display, and is currently being wed as an on-chip video coprocessor in the TM-2700 digital television chip. TM-8700 is the second generation of an architectural family of programmable multimedia processors from Philips Semiconductors and it not only supports all ATSC formats, from standard-definition to wide-angle high-definition video, but has also the power to handle High-Definition Television (HDTV) video and audio source decoding (high-level MPEG-2, AC-3 and ProLogic audio, closed captioning, etc.) as well as the flexibility to process advanced interactive services.