Multimedia resource library using video streaming

A t a time when all organizations are searching for new ways to work, think and learn more efficiently and effectively, interactive technologies of all kinds are being employed. One of these technologies is live, two-way, audio and video communication. Twoway interactive video isn't new, but how it is done has taken a new turn. As opposed to transmission of analog signals, video is now being compressed and converted to digital information which requires far less space on a cartier whether it be satellite, microwave or terrestrial lines. This compressed video (CV) can be transmitted at a variety of rates. Businesses have used compressed video since the early 1980's to cut travel time and costs for the purposes of training and videoconferencing. Educational institutions have also begun to explore the potential of CV in the past few years.

The significant frame size variability exhibited in the compressed videos imposes a great challenge on network delivery. In this paper we propose an efficient flow control scheme, employed in the peer stations (i.e., servers and clients), for delivery of prestored compressed videos in a video-on-demand (VOD) system. This scheme resorts to an off-line analysis on the video frame sizes and server properties for figuring out the necessary buffer space and network bandwidth. The server platform of particular interest obeys a cycle-based data-block retrieval discipline, which is an essential technique to reduce the disk seek time for leveraging the disk throughput for supporting a large number of concurrent video accesses. Such a discipline is taken into account here to guarantee smooth delivery of variable-bit-rate videos. In run-time a server-driven control model is in use, where a server performs the primary flow control task, without relying on any feedback from clients. The scheme has been implemented in our prototype VOD system to support both unicast- and multicast communication paradigms under an RSVP-enabled network.

Many advanced video applications require manipulations of compressed video signals. Popular video manipulation functions include overlap (opaque or semitransparent), translation, scaling, linear filtering, rotation, and pixel multiplication. We propose algorithms to manipulate compressed video in the compressed domain. Specifically, we focus on compression algorithms using the discrete cosine transform (DCT) with or without motion compensation (MC). Such compression systems include JPEG, motion JPEG, MPEG, and H.261. We derive a complete set of algorithms for all aforementioned manipulation functions in the transform domain, in which video signals are represented by quantized transform coefficients. Due to a much lower data rate and the elimination of decompression/compression conversion, the transform-domain approach has great potential in reducing the computational complexity. The actual computational speedup depends on the specific manipulation functions and the compression characteristics of the input video, such as the compression rate and the nonzero motion vector percentage. The proposed techniques can be applied to general orthogonal transforms, such as the discrete trigonometric transform. For compression systems incorporating MC (such as MPEG), we propose a new decoding algorithm to reconstruct the video in the transform domain and then perform the desired manipulations in the transform domain. The same technique can be applied to efficient video transcoding (e.g., from MPEG to JPEG) with minimal decoding. >

The subject of study. A new algorithm for real-time data compression in a video surveillance system from an unmanned aerial vehicle is proposed. The algorithm makes it possible to obtain different degrees of video data compression using different codecs for specific video recording conditions (UAV altitude and flight speed, nature of the observed terrain, etc.), and also provides the ability to transmit video data to the flight control point via radio channel. A detailed analysis of the quality of the proposed algorithm for shooting in various video formats that meet international standards (HD and Full HD) was carried out, both in terms of visual perception and objective criteria. The purpose of the study was to conduct a comprehensive research on the problem of improving the quality of the video surveillance system from the UAV and to create a video compression algorithm that allows broadcasting video data to the control center in real time with high quality. Objectives. To develop and programmatically implement a video compression algorithm based on modern codecs for video surveillance sys­tems on board a UAV. To use modern video cameras and a microcomputer platform as hardware. To analyze the quality indicators of the algorithm in different conditions. Methods used. Evaluation of visual perception of video compression results, statistical analysis and processing of the parameters of the analyzed video sequence as a random spatio-temporal process. Results. A new video data compression algorithm for the UAV video surveillance system based on the Raspberry Pi 5 microcomputer using modern video compression codecs H.264 and H.265 was synthesized. The algorithm provides the required compression ratio and data transmission rate over the radio channel between the UAV and the flight control center. Detailed estimates of the quality of the algorithm are obtained. Conclusions. Scientific novelty of the results: A new universal video data compression algorithm for video surveillance systems on board UAVs with the ability to transmit video over a radio channel in real time has been proposed and implemented. The quality of the proposed algorithm is verified on real video surveillance results. The proposed algorithm is implemented in the Python software environment.

Compressed digital video is becoming prevalent for video storage and communication. It is often desirable to perform various video processing tasks on compressed video streams, however, operations once considered simple, such as splicing and reverse play, are much more complex in the context of compressed video. The conceptually-simple approach of completely decoding the video into the pixel domain, performing the processing, and re-encoding the entire video sequence is often inefficient and impractical. This paper investigates the problem of transcoding compressed video data from one standards-compliant data stream to another. The goal is to develop video transcoding algorithms that achieve high performance with computational efficiency. In particular, this paper presents efficient algorithms for splicing and reverse play of compressed video streams.

Advances in fiber optics, video compression and transmission technologies are driving the development and deployment of a new generation of CATV equipment. Optical fiber super- trunks are now expanding program delivery capabilities to over 150 channels and digital video compression will soon increase the capacity of existing satellite, fiber and cable systems by a factor of 5 to 10 or more. These next generation CATV systems will support Video on Demand (VOD) and advanced communications services such as broad band LAN/WAN networking and Personal Communications Services (PCS).

The demand for video through over-the-top (OTT) has been constantly increasing in recent years. During the COVID-19 pandemic, demand skyrocketed, hence leading to the need for better video compression. The human visual system (HVS) can quickly select visually important regions in its visual field. These regions are captured at high resolution, while other peripheral regions receive little attention. Saliency maps are a way to imitate the HVS attention mechanism. Recently, deep learning-based saliency models have achieved tremendous improvements. This article leverages state-of-the-art deep learning-based saliency models to improve video coding efficiency. First, a saliency-based rate control scheme is integrated in a high-efficiency video encoder (HEVC). Then, a saliency-guided preprocessing filtering step is introduced. Finally, the two approaches are combined. Objective and subjective evaluations show that it can lower the bitrate from 6% to almost 30% while maintaining the same visual quality.

Block coding based on the discrete cosine transform (DCT) is very popular in image and video compression. Pre/post-filtering can be attached to a DCT-based block coding system to improve coding efficiency as well as to mitigate blocking artifacts. Previously designed pre/post-filters are optimized to maximize coding efficiency solely. For image and video communication over unreliable channels, those pre/post-filters are sensitive to transmission errors. This paper addresses the problem of designing pre/post-filters which are more error resilient. Reconstruction performance is measured by how low the average reconstruction error is, and how uniformly the reconstruction error is distributed. A family of pre/post-filters is designed to provide desired tradeoffs between coding efficiency and robustness to transmission errors. Experiments show that these filtering operators can achieve superior reconstruction performance without sacrificing much coding performance.

Scalable video coding is important in a number of applications where video needs to be decoded anddisplayed at a variety of resolution scales. It is more efficient than simulcasting, in which all desired resolution scales are coded totally independent of one another within the constraint of a fixed available bandwidth. In this paper, we focus on scalability using the frequency domain approach. We employ the frameworkproposed for the ongoing second phase of Motion Picture Experts Group (MPEG-2) standard to study theperformance of one such scheme and investigate improvements aimed at increasing its efficiency. Practicalissues related to multiplexing of encoded data of various resolution scales to facilitate decoding are considered. Simulations are performed to investigate the potential of a chosen frequency domain scheme. Various prospectsand limitations are also discussed. 1. INTRODUCTION Much of the recent work on video compression has focused on improving performance of video codingschemes consisting of a single layer [1,2]. There are, however, a range of applications where video needs to bedecoded and displayed at a variety of resolution scales. Among the noteworthy applications of interest [3,5] aremulti-point video conferencing, windowed display on workstations, video communications on asynchronoustransfer mode (ATM) networks and FIDTV with embedded standard TV. In the light of abundance of applica-tions that may benefit from multi-resolution video, the second phase of Motion Pictures Experts Group

Recent advances in high-performance portable processing equipment, such as mobile pro‐ cessors, have enabled users to experience new-generation devices, including networked gaming consuls, smart televisions and smart phones. Video coding, video compression and video communication are essential parts of the aforementioned applications. However, networking infrastructures do not offer unlimited bandwidth, and storage devices do not offer unlimited capacities. Therefore, there is significant demand for reliable high-performance video communication/compression protocols. Video compression refers to the process of reducing the amount of video data used to represent digital videos; it is a combination of spatial image compression and temporal motion compensation (Hanzo et al., 2007).

The existing DONet-based approach uses successfully a random gossip algorithm for scalable live video streaming. This pure mesh overlay network-based solution may lead to unacceptable latency or even failure of VCR operations in video-on-demand (VoD) services where nodes usually have different playing offsets, across a wide range. This paper proposes DONet-VoD which enhances DONet in order to address issues related to VoD delivery and VCR operations. In DONet-VoD, DONet principle is employed for the video distribution over the overlay network and a novel algorithm which uses a multi-way tree structure and extra prefetching buffers at the nodes is proposed to support efficient VoD operations. Video segments are prefetched and stored in a distributed manner in the nodespsila prefetching buffer along the tree. The cooperation between DONet-based video delivery and the tree-located multimedia components enable multimedia streaming interactive commands to be performed efficiently. This paper presents and discusses the prefetching scheme, details the cooperation procedure, and then analyses the performance of the proposed DONet-VoD.

In the present work a feasibility study of a novel peer-to-peer architecture for live video streaming is proposed. It leverages both recent web technologies for peer implementation and overlay management, and the use of online social networks as integral part of the architecture itself.

compressed domain processing, transcoding, video editing, MPEG, splicing, reverse play, frame rate conversion, interlace to progressive, motion vector resampling Video compression algorithms are being used to compress digital video for a wide variety of applications, including video delivery over the Internet, advanced television broadcasting, video streaming, video conferencing, and video storage and editing. The impressive performance of modern compression algorithms, combined with the growing availability of video encoders and decoders and low-cost computers, storage devices, and networking equipment, makes it evident that between video capture and video playback, video will be handled in compressed video form. The resulting end-toend compressed digital video systems motivate the need to develop efficient algorithms for handling compressed digital video.

In several multimedia applications, image and video coding plays a significant role. In many applications, sceneries such as broadcast services over satellite and terrestrial channels, digital video storage, wires and wireless conversational services and digital video communication are used. It is not possible to store full digital video without processing. Video compression reduces the data used to represent digital video images and is a combination of spatial image compression and temporal motion compensation. The main aim of our research is to develop an efficient video compression system. The proposed system consists of three steps. At first, wavelet decomposition is applied to the I–frame and the resulting coefficients are quantised using the listless SPECK (LSK) algorithm. In the next step, motion estimation is done using adaptive rood search with the spatio–temporal correlation method (ARS–ST) and it calculates the distance between the P–frame and I–frame blocks. In the final step, the difference between the original and the predicted P–frame is evaluated and is known as residual. To produce good quality predictive frames, this residual is transmitted along with motion vectors (MVs) and to decrease its size, it is coded using LSK. The proposed video compression technique uses different videos for assessment and by determining the PSNR values, compression efficiency is estimated. By comparing the proposed system with existing systems, it is seen that our system effectively compresses videos with remarkable PSNR measurements.

Image and video coding plays a salient role in numerous multimedia applications. Nowadays, digital video communication is adopted in many applications, for example broadcast services over satellite and terrestrial channels, digital video storage, and more. It is impractical for us to store the full digital video without processing. It is well-known that video compression is a process of diminishing the size of data used to depict digital video images, and it is an amalgamation of spatial image compression and temporal motion compensation. The primary intent of our research is to propose a very potent video compression system. Originally, wavelet decomposition is applied to the I-frame and the resulting coefficients are quantized with the aid of Listless SPECK (LSK) algorithm. Secondly, an adaptive rood search with spatio-temporal correlation technique (ARS-ST) is employed for motion estimation and it finds the displacement of the blocks of P-frame from the I-frame. The proposed video compression technique is evaluated by using different videos and the compression efficiency is computed by determining the peak signal to noise ratio (PSNR) values. Also, the efficacy of the proposed system is compared with existent video compression systems. From the comparative study, it can be seen that our proposed system robustly compress the videos with adequate PSNR measurements.