Embedded Video Surveillance System Based on H.264

In modern world more image and video compression technologies improving gradually. But major barrier of the new technologies concepts are repeated from authors. Because authors can’t find in depth of the papers from various technologies. So Survey of Video Compression Techniques very helpful for these types problems in video compression areas. Video compression techniques such as DCT coding, Quantization, Entropy coding, Motion estimation are widely used in video compression techniques. The main focus of this paper is to analyse video compression techniques required for video processing especially to discover how much amount of data to compressed, which techniques is faster and visual quality better and so on. We evaluate the video compression techniques for finding compression ratio in terms of performance, speed and accuracy. We also compare video compression techniques with conventional methods.

Compression of images process is a step in the image processing. It is concerned with the transmission and storage of digitally created images. Fractal coding is a potential image and video compression approach with good reconstruction fidelity and relatively large compression ratios, because of its simplicity and great performance; fractal image compression (FIC) is a particularly popular approach in image compression applications. However, it has a significant disadvantage in the form of a long encoding time. This is because encoding any small bit necessitates a massive similarity search in the original data As a result; the FIC search time is reduced while the quality of the reconstructed images is maintained acceptable level in many introduced paper and other still a study topic in progress. Fractal images are images that are self-similar in that each individual part is the same as the total. This paper will discusses many attempts for more author that working on image and video compression using fractal compression technique based on various approach and with each discuss focuses on the main parameter of compression such compression ratio (CR), peak signal to noise ratio (PSNR) and encoding time, as well as the details of data set the used for testing also writing with each technique to creating fractal video and image compression. Index Terms— FIC, Video compression, parallel processing, Iteration Function System (IFS), Image processing.

Video compression has great significance in the communication of motion pictures. Video compression techniques try to remove the different types of redundancy within or between video sequences. In the temporal domain, the video compression techniques remove the redundancies between the highly correlated consequence frames of the video. In the spatial domain, the video compression techniques remove the redundancies between the highly correlated consequence pixels (samples) in the same frame. Evolving neural-networks based video coding research efforts are focused on improving existing video codecs by performing better predictions that are incorporated within the same codec framework or holistic methods of end-to-end video compression schemes. Current neural network-based video compression adapts static codebook to achieve compression that leads to learning inability from new samples. This paper proposes a modified video compression model that adapts the genetic algorithm to build an optimal codebook for adaptive vector quantization that is used as an activation function inside the neural network’s hidden layer. Background subtraction algorithm is employed to extract motion objects within frames to generate the context-based initial codebook. Furthermore, Differential Pulse Code Modulation (DPCM) is utilized for lossless compression of significant wavelet coefficients; whereas low energy coefficients are lossy compressed using Learning Vector Quantization (LVQ) neural networks. Finally, Run Length Encoding (RLE) is engaged to encode the quantized coefficients to achieve a higher compression ratio. Experiments have proven the system’s ability to achieve higher compression ratio with acceptable efficiency measured by PSNR.

As video technology continues to seamlessly weave itself into the fabric of daily life, there is a growing need for enhanced storage and efficient video transmission. This surge in demand has led to heightened expectations and standards for video compression technology. Machine learning as an up-and-coming technology can play its advantages in the field of video compression. This article reviews the current state of research on combining video compression techniques with machine learning. The article provides an overview of various research avenues for enhancement, spanning from conventional video compression algorithms to the fusion of traditional compression frameworks with machine learning methodologies, and even the development of novel end-to-end compression algorithms. In additional, the article explores the possible various application scenarios of machine learning-based video compression algorithms based on the characteristics of such non-standard and arithmetic demanding algorithms. At the end, the article speculates on the future of video compression algorithms based on the content of the various studies reviewed in the article.

Encoder adaptable difference detection for low power video compression in surveillance system

Video signals tend to contain significant temporal compression because of the high degree of correlation between consecutive frames. This means that existing video compression technologies have not utilized all the information that is readily available in such signals. In particular, we will discuss a novel video compression technique that is capable of exploiting temporal redundancy to improve the details while processing requirements are kept low. In our case, the transformation is applied by converting the 3D video data into 2D space. This transformation allows for applying 2D transforms to search for temporal redundancy and makes motion compensation which is rather expensive computationally unnecessary. This transformation combines the spatial and temporal relations of the video signal and provides each group of pictures (GOP) with a single image with higher spatial relations. We then use SPIHT to increase the compression efficiency as a result of the properties of wavelet-transformed images. The use of discrete wavelet transform (DWT) provides a level of de-correlation that allows effective energy compaction to take place resulting in high video compression ratios. A wide range of experiments clearly indicates the potential of our technique especially when the bit rates are high and with slow-motion videos.

Over the last one and a half decades, digital video compression technologies have become an integral part of the way we create, communicate, and consume visual information. In this paper, techniques for video compression are reviewed, starting from basic concepts. The rate-distortion performance of modern video compression schemes is the result of an interaction between motion representation techniques, intra-picture prediction techniques, waveform coding of differences, and waveform coding of various refreshed regions. The paper starts with an explanation of the basic concepts of video codec design and then explains how these various features have been integrated into international standards, up to and including the most recent such standard, known as H.264/AVC.

The overall objective of the project was to develop fast decompression and playback routines for full motion, color video. A survey was done of existing standards and potential new routines and techniques for video compression and playback to determine which were suitable for use on the Army Contact Test Set (CTS) III computer. Playback speeds were preferred to be at 30 frames per second on the target architecture. Compression ratios were expected to store several minutes of synchronized video in 1 MB. Playback routines were Windows 3.1 compatible. Identification of any hardware upgrades assoaated with playback were made when necessary. A demonstration of existing LLNL algorithms for still frame decompression which represented a current benchmark of data compression performance was completed. NOVA Management and LLNL used some of the compression technologies developed or currently being developed at LLNL to address the requirements of this SBIR. LLNL started by evaluating current compression technologies or combinations thereof to estimate the best compression ratio LLNL could achieve at this stage. LLNL ported some of our software from the SUN workstation environment to the PC or equivalent environment (e.g., MAC) so as to closely match the computer environment suggested by the Army. Finally, LLNL designed and outlined new algorithms to meet the desired video compression of CTS.

The purpose of this CRADA was to establish a framework for LLNL and NOVA Management, Inc. to Cooperatively develop video compression routines to be used to condense maintenance documentation provided on computers issued to Army mechanics to help them diagnose and repair equipment faults. In the past, paper technical manuals were the only means of communicating fault isolation, diagnostics and repair action information to maintenance personnel. The need for this information was at the weapon system itself as well as at the maintenance depot. This project addressed the need for means of efficient storage and retrieval of video (bandwidth issue) by maintenance personnel as they moved through the diagnostic procedures. Several research and development projects have employed interactive stored video as a means of providing maintenance information to the soldier. The problem with this approach was the massive data storage requirement. The overall objective of the project was to develop fast decompression and playback routines for full motion, coIor video. A survey was done of existing standards and potential new routines and techniques for video compression and playback to determine which were suitable for use on the CTS. Playback speeds were preferred to beat 30 frames per second on the target architecture. Compression ratios were expected to store several minutes of synchronized video in 1 MB. Playback routines were Windows 3.1 compatible. Identification of any hardware upgrades associated with playback were made when necessary. A demonstration of existing LLNL algorithms for still frame decompression which represented a current benchmark of data compression performance was completed. NOVA Management and LLNL used some of the compression technologies developed or currently being developed at LLNL to address the requirements of this SBIR. LLNL started by evaluating current compression technologies or combinations thereof to estimate the best compression ratio LLNL could achieve at this stage. LLNL ported some of our software from the SUN workstation environment to the PC or equivalent environment (e.g., MAC) so as to closely match the computer environment suggested by the Army. Finally, LLNL designed and outlined new algorithms to meet the desired video compression of CTS.

This paper presents a new technique for region-oriented video compression based on the minimization of a minimum description length (MDL) criterion. The proposed video compression scheme is based on a region-based motion compensation approach in which the displaced frame difference is partially coded together with the segmentation and the motion parameters. The region-based segmentation is approximated by a polygonal representation which permits to significantly reduce the segmentation coding cost. The polygonal approximation which minimizes the MDL criterion is finally considered as optimal. Experimental results are presented on real video sequences.

Processing of the video signal prior to MPEG2 encoding can provide dramatic quality improvements in the subjective quality of the reconstructed video or bit-rate reductions in the generation of the compressed bit-stream. In a broadcast environment the video source is not guaranteed to be noise free. Noise in video signal will not only reduce video quality but also cost extra bits to code. Instead of using expensive external video pre-processors, state of the art broadcast quality real-time video encoders incorporate onboard video pre-processing to remove noise. An estimate of the noise level is essential for the control of subsequent filtering. Optimum performance is achieved when there exists a close coupling between the video preprocessor and the video encoder. This paper presents noise level estimation techniques in the design of on-board video preprocessor for real-time video encoders. Simulation results are presented along with implementation considerations.

Automatic natural scene understanding and annotating regions with semantically meaningful labels, such as road or sky, are key aspects of image and video analysis. The annotation of regions is a considered helpful for improving the object-of-interest detection because the object position in the scene is also exploited. For a reliable model of a scene and associated context information, the labeling task involves image analysis at multiple, both global and local, scene levels. In this paper, we develop a general framework for performing automatic semantic labeling of video scenes by combining the local features and spatial contextual cues. While maintaining a high accuracy, we pursue an algorithm with low computational complexity, so that it is suitable for real-time implementation in embedded video surveillance. We apply our approach to a complex surveillance use case and to three different datasets: WaterVisie [1], LabelMe [2] and our own dataset. We show that our method quantitatively and qualitatively outperforms two sate-of-the-art approaches [3][4].

In real-time video communications, the rate control strategies must be utilized to satisfy the end-to-end delay and prevent the encoding buffer from over/underflow. In other words, to acquire the best possible video quality with a minimal quality variation in the playback video, an accurate rate-distortion (R-D) model of the video source is critical in optimizing the bit allocation for video coding. In the paper, an exponential functions based R-D model is proposed for intra-coded frames in video coding. Numerous experiments have consistently shown that the proposed model outperforms other popular R-D models in terms of both the estimation accuracy and computation complexity, making it suitable for rate control in real-time video coding.

The huge usage of digital multimedia via communications, wireless communications, Internet, Intranet and cellular mobile leads to incurable growth of data flow through these Media. The researchers go deep in developing efficient techniques in these fields such as compression of data, image and video. Recently, video compression techniques and their applications in many areas (educational, agriculture, medical …) cause this field to be one of the most interested fields. Wavelet transform is an efficient method that can be used to perform an efficient compression technique. This work deals with the developing of an efficient video compression approach based on frames difference approaches that concentrated on the calculation of frame near distance (difference between frames). The selection of the meaningful frame depends on many factors such as compression performance, frame details, frame size and near distance between frames. Three different approaches are applied for removing the lowest frame difference. In this paper, many videos are tested to insure the efficiency of this technique, in addition a good performance results has been obtained.

We propose a video compression technique based on neural networks which is completely different from existing techniques. Existing video compression techniques have used a neural network as a video compressor. In other words, video compression flow of the existing techniques was: an original video is inputted to a neural network, and the neural network outputs compressed expression of the video. In this paper, we propose a technique for storing a video into a neural network. In this technique, the neural network is treated as a compressed expression of the video. Our purpose is to find the configuration of the neural network that stores and compresses the video efficiently. We implement two models of the neural network. One is a multilayer perceptron whose input is t, and output is \(frame_t\). The other is the Long short-term memory (LSTM) whose inputs are past frames like \(frame_{t - 2}\) and \(frame_{t - 1}\), and output is predicted \(frame_t\). If a data amount required to express the neural network is smaller than the video, it can be regarded as a compressed video. As the result of experiments, it is shown that the data amount of the LSTM implementation was larger than the original video. However, that of the multilayer perceptron one was about a half of the original video, and value of the structural similarity (SSIM), which is a metric of image quality, was about 0.99.