Compressive Video Sensing Based on Intra-Inter-Frame Constraints and Genetic Algorithm

Establishing structured reconstruction models and efficient reconstruction algorithms according to practical engineering needs is of great concern in the applied research of Compressed Sensing (CS) theory. Targeting problems during high-speed video capture, the paper proposes a set of video CS scheme based on intra-frame and inter-frame constraints and Genetic Algorithm (GA). Firstly, it employs the intra-frame and inter-frame correlation of the video signals as the priori information, creating a video CS reconstruction model on the basis of temporal and spatial similarity constraints. Then it utilizes overcomplete dictionary of Ridgelet to divide the video frames into three structures, smooth, single-oriented, or multijointed. Video frames cluster according to the structure using Affinity Propagation (AP) algorithm, and finally clusters are reconstructed using evolutionary algorithm. It is proved efficient in terms of reconstruction result in the experiment.

Versatile Video Coding for 3.0 Next Generation Digital TV in Brazil

In the past few decades, the video broadcast ecosystem has gone through major changes; Originally transmitted using analog signals, it has been more and more transitioned toward digital, leveraging compression technologies and transport protocols, principally developed by MPEG. Along this way, the introduction of new video formats was achieved with standardization of new compression technologies for their better bandwidth preservation. Notably, SD with MPEG-2, HD with H.264, 4K/UHD with HEVC. In Brazil, the successive generations of digital broadcasting systems were developed by the SBTVD Forum, from TV-1.0 to TV-3.0 nowadays. The ambition of TV-3.0 is significantly higher than that of previous generations as it targets the delivery of IPbased signals for applications, such as 8K, HDR, virtual and augmented reality. To deliver such services, compressed video signals shall fit into a limited bandwidth, requiring even more advanced compression technologies. The Versatile Video Coding standard (H.266/VVC), has been finalized by the JVET committee in 2021 and is a relevant candidate to address the TV3.0 requirements. VVC is versatile by nature thanks to its dedicated tools for efficient compression of various formats, from 8K to 360°, and provides around 50% of bitrate saving compared to its predecessor HEVC. This paper presents the VVC-based compression system that has been proposed to the SBTVD call for proposals for TV-3.0. A technical description of VVC and an evaluation of its coding performance is provided. In addition, an end-to-end live transmission chain is demonstrated, supporting 4K real-time encoding and decoding with a low glass-to-glass latency.

Compressed Video Quality Index Based on Saliency-Aware Artifact Detection

Video coding technology makes the required storage and transmission bandwidth of video services decrease by reducing the bitrate of the video stream. However, the compressed video signals may involve perceivable information loss, especially when the video is overcompressed. In such cases, the viewers can observe visually annoying artifacts, namely, Perceivable Encoding Artifacts (PEAs), which degrade their perceived video quality. To monitor and measure these PEAs (including blurring, blocking, ringing and color bleeding), we propose an objective video quality metric named Saliency-Aware Artifact Measurement (SAAM) without any reference information. The SAAM metric first introduces video saliency detection to extract interested regions and further splits these regions into a finite number of image patches. For each image patch, the data-driven model is utilized to evaluate intensities of PEAs. Finally, these intensities are fused into an overall metric using Support Vector Regression (SVR). In experiment section, we compared the SAAM metric with other popular video quality metrics on four publicly available databases: LIVE, CSIQ, IVP and FERIT-RTRK. The results reveal the promising quality prediction performance of the SAAM metric, which is superior to most of the popular compressed video quality evaluation models.

An Adaptive Rate Blocked Compressive Sensing Method for Video

An adaptive rate Compressive Sensing (CS) method for video signals is proposed. The Blocked Compressive Sensing (BCS) scheme is adopted in this method. Firstly, each video frame is blocked and measured by the BCS scheme, and then the mean and variance of each image block are estimated by observing the CS measurement results. Using the mean and variance of each image block, the sparsity of the block is estimated and then the block can be classified. Adaptive rate sampling is realized by assigning different sampling rates to different classes. At the same time, in order to make better use of the correlation between video frames, a reference block subtraction method is also designed in this paper, which uses the estimates of the sparsity of image blocks as the basis for the reference block update. All operations of the proposed method only depend on the CS measurement results of image blocks and all calculations are simple. Thus, the proposed method is suitable for implementation in CS sampling devices with limited computational performance. Experiment results show that, compared with the actual values, the sparsity estimates and block classification results of the proposed method are accurate. Compared with the latest adaptive Compressive Video Sensing methods, the reconstructed image quality of the proposed method is better.

Reducing Buffering Delay Using Memory Arbitration Mechanism for IPTV Based Applications

High-speed communication needs high data transfer capacity and low latency, which are the key parameters of high-speed communication. Converging different applications such as IPTV to high-speed networks requires high transmission capacity and low delay with good QoS. Delays related to IPTV are video buffering, synchronization, and switching delay that obstructs the client's excellent quality assistance. In an application like IPTV, the video signals are buffered (happened to be in near end routers), and they are recombined for the client when it is asserted. To achieve the above stated, memory banks are deployed in a set top box that is used to buffer the video signals that enter in, thereby reducing expected delay. Playback mechanism is also included along with the proposed model to accomplish a better outcome. Proposed RTL schematic design was simulated using Verilog, executed in Model Sim – Altera 10.1b (Quartus II 12.1 edition) and Cadence 5.

An Innovative Approach for Denoising of the Digital Video Stream

Everyday tones of video signals are generated, transmitted, and analyzed. The video contents are created for educational purposes, entertainment purposes, surveillance purposes, medical imaging purposes, weather forecasting, satellite imaging, and many other significant places. During the different phases of video content preparation, transmission, and analysis some unwanted signals get interfered with the true contents. Particularly, the medical imaging signals, since they are weak signals, are more prone to unwanted interferences. Such unwanted interference of the noise signals makes it difficult to analyze the critical information in the video contents and hence, the need for denoising process arises. A decent video denoising framework assures visual improvement in the video signals or it serves as the significant pre-processing step in the video processing steps like compression and analysis. Through this paper, we are about to disclose an efficient video denoising framework that takes the noisy video signal in the form of frames per second and performs the video denoising using shot detection, compensation, intensity calculations, and motion estimation process.

Highly Information and Energy-efficient Monitoring Data Transmission in IoT Networks

The components of technologies of increasing information and energy efficiency of IoT monitoring networks subscriber systems with protected transfer functioning of reliable packets of information with the increased information capacity are described. In the places of formation of network flows, i.e. in the places of installation of object and on-board systems the realization of a complex of adaptive filtering algorithms, compression and protection of samples of monitoring signals and video data frames with the subsequent adaptive formation and transmission of highly informative code-signal sequences is offered. It is proposed a signal approach as a basis for inputting and compact coding of signals and video data frames of the reliable samples. The signal approach is proposed, according to which the amplitude-time or amplitude-number parameters of the most informative samples of signals and video signals are determined in the rate of input of monitoring data. These are extremes and points of inflection or points of curve movement change. The obtained data are subject to data compression with controlled information loss and lossless compression-protection. According to the proposed information technology for building effective IoT networks for crypto protection of monitoring data arrays by processors of object and on-board systems, the use of disposable ciphers is proposed, which are the rules and parameters for generating crypto-resistant pseudo-random data arrays of a certain length. These rules and parameters are known only to the subscriber-transmitter and the subscriber-receiver of information packets and are used by network subscribers in the process of data compression with losses and without losses, in the process of crypto-resistant and noise-resistant information packets forming of limited duration with increased information capacity. Energy-efficient data packet transmission is based on a significant reduction in the output streams of protected highly informative monitoring data packets and the implementation by object and on-board systems processors a set of algorithms for processing, encoding, encrypting and transmitting data minimized by computational complexity.

Image Sensors for Wave Monitoring in Shore Protection: Characterization through a Machine Learning Algorithm

Waves propagating on the water surface can be considered as propagating in a dispersive medium, where gravity and surface tension at the air–water interface act as restoring forces. The velocity at which energy is transported in water waves is defined by the group velocity. The paper reports the use of video-camera observations to study the impact of water waves on an urban shore. The video-monitoring system consists of two separate cameras equipped with progressive RGB CMOS sensors that allow 1080p HDTV video recording. The sensing system delivers video signals that are processed by a machine learning technique. The scope of the research is to identify features of water waves that cannot be normally observed. First, a conventional modelling was performed using data delivered by image sensors together with additional data such as temperature, and wind speed, measured with dedicated sensors. Stealth waves are detected, as are the inverting phenomena encompassed in waves. This latter phenomenon can be detected only through machine learning. This double approach allows us to prevent extreme events that can take place in offshore and onshore areas.