Performance Analysis of Iteratively Decoded Convergent Source Mapping with Sphere Packing-Assisted Differential Space-Time Spreading Technique for Efficient Video Transmission

With the substantial growth in number of wireless devices, future communication demands overarching research to design high-throughput and efficient systems. We propose an intelligent Convergent Source Mapping (CSM) approach incorporating Differential Space-Time Spreading (DSTS) technique with Sphere Packing (SP) modulation. The crux of CSM process is assured convergence by attaining an infinitesimal Bit-Error Rate (BER). Data Partitioning (DP) H.264 video codec is deployed to gauge the performance of our intelligent and efficient system. For the purpose of efficient and higher data rates, we have incorporated compression efficient source encoding along with error resiliency and transmission robustness features. The proposed system follows the concept of iterations between the Soft-Bit Source-Decoder (SBSD) and Recursive Systematic Convolutional (RSC) decoder. Simulations of the DSTS-SP-assisted CSM system are presented for the correlated narrowband Rayleigh channel, using different CSM rates but constant overall bit-rate budget. The SP-assisted DSTS systems are mainly useful in decoding algorithms that operate without requiring Channel State Information (CSI). The effects of incorporating redundancy via different CSM schemes on the attainable performance and convergence of the proposed system are investigated using EXtrinsic Information Transfer (EXIT) charts. The effectiveness of the proposed system is demonstrated through IT++ based proof-of-concept simulations. The Peak Signal-to-Noise Ratio (PSNR) analysis shows that using Rate-2/6 CSM with minimum Hamming distance ( d H , min ) of 4 offers about 5 dB gain, compared to an identical overall system code rate but with Rate-2/3 CSM and d H , min of 2. Furthermore, for a consistent value of d H , min and overall rate, the Rate-2/3 CSM scheme beats the Rate-5/6 CSM by about 2 dB at the PSNR degradation point of 2 dB. Moreover, the proposed system with Rate-2/3 CSM scheme furnishes an E b / N 0 gain of 20 dB when compared with the uniform-rate benchmarker. Clearly, we can say that higher d H , min and lower CSM values are favourable for our proposed setup.

Approximation Algorithm for Scheduling a Chain of Tasks for Motion Estimation on Heterogeneous Systems MPSoC

Co-design embedded system are very important step in digital vehicle and airplane. The multicore and multiprocessor SoC (MPSoC) started a new computing era. It is becoming increasingly used because it can provide designers much more opportunities to meet specific performances. Designing embedded systems includes two main phases: (i) HW/SW Partitioning performed from high-level (eclipse C/C++ or python (machine learning and deep learning)) functional and architecture models (with virtual prototype and real prototype). And (ii) Software Design performed with significantly more detailed models with scheduling and partitioning tasks algorithm DAG Directed Acyclic Graph and GGEN Generation Graph Estimation Nodes (there are automatic DAG algorithm). Partitioning decisions are made according to performance assumptions that should be validated on the more refined software models for ME block and GGEN algorithm. In this paper, we focus to optimize a execution time and amelioration for quality of video with a scheduling and partitioning tasks in video codec. We show how they can be modeled the video sequence test with the size of video in height and width (three models of scheduling tasks in four processor). This modeling with DAG and GGEN are partitioning at different platform in OVP (partitioning, SW design). We can know the optimization of consumption energy and execution time in SoC and MPSoC platform.

Analisis Perbandingan Teknik Video Codec H.264/AVC, H.265/HEVC, VP9 dan AV1

Video applications consume more energy on the Internet and can be accessed by electronic devices, due to an increase in the consumption of high-resolution and high-quality video content, presenting serious issues to delivery infrastructure that needs higher video compression technologies. The focus of this paper is to evaluate the quality of the most current codec, AV1, to its predecessor codec. The comparison was made experimentally at two video resolutions (1080p and 720p) by sampling video frames with various CRF/CQP values and testing several parameters analyses such as encoding duration, compression ratio, bit rate, Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The AV1 codec is very great in terms of quality and file size, even though it is slower in terms of compression speed. The H.265/HEVC codec, on the other side, beats the other codec in terms of compression ratio. In conclusion, the H.265/HEVC codec is suggested as a material for obtaining a well compressed video with small file size and a short time.

Optimization of Video Cloud Gaming Using Fast HEVC Video Compression Technique

Cloud computing is a model of technology that offers access to system resources with advanced level of services ability. These resources are measured reliable, flexible and affordable for several kinds of applications and users. Gaming manufacturing is one filed that expands the profits of cloud computing as numerous new cloud gaming designs have been presented. Many advantages of cloud gaming have exaggerated the success of gaming based on the improvements on traditional online gaming. Though, cloud gaming grieves from several downsides such as the massive amount of needed video processing and the computational complexity required for that. This paper displays the original system drawbacks and develops a new and original algorithm to speed up the encoding process by reduces the computational complexity by exploiting the block type and location. Enhancements on the video codec led to 12.2% speeding up on the over-all encoding time with slight loss of users’ satisfactions. Keywords: Cloud gaming, Computational complexity, Motion estimation, HEVC, Video Encoding

Deceptive Techniques to Hide a Compressed Video Stream for Information Security

With the recent development of video compression methods, video transmission on traditional devices and video distribution using networks has increased in various devices such as drones, IP cameras, and small IoT devices. As a result, the demand for encryption techniques such as MPEG-DASH for transmitting streams over networks is increasing. These video stream security methods guarantee stream confidentiality. However, they do not hide the fact that the encrypted stream is being transmitted over the network. Considering that sniffing attacks can analyze the entropy of the stream and scan huge amounts of traffic on the network, to solve this problem, the deception method is required, which appears unencrypted but a confidential stream. In this paper, we propose the new deception method that utilizes standard NAL unit rules of video codec, where the unpromised device shows the cover video and the promised device shows the secret video for deceptive security. This method allows a low encryption cost and the stream to dodge entropy-based sniffing scan attacks. The proposed stream shows that successful decoding using five standard decoders and processing performance was 61% faster than the conventional encryption method in the test signal conformance set. In addition, a network encrypted stream scan method the HEDGE showed classification results that our stream is similar to a compressed video.

SAVSDN: A Scene-Aware Video Spark Detection Network for Aero Engine Intelligent Test

Due to carbon deposits, lean flames, or damaged metal parts, sparks can occur in aero engine chambers. At present, the detection of such sparks deeply depends on laborious manual work. Considering that interference has the same features as sparks, almost all existing object detectors cannot replace humans in carrying out high-precision spark detection. In this paper, we propose a scene-aware spark detection network, consisting of an information fusion-based cascading video codec-image object detector structure, which we name SAVSDN. Unlike video object detectors utilizing candidate boxes from adjacent frames to assist in the current prediction, we find that efforts should be made to extract the spatio-temporal features of adjacent frames to reduce over-detection. Visualization experiments show that SAVSDN can learn the difference in spatio-temporal features between sparks and interference. To solve the problem of a lack of aero engine anomalous spark data, we introduce a method to generate simulated spark images based on the Gaussian function. In addition, we publish the first simulated aero engine spark data set, which we name SAES. In our experiments, SAVSDN far outperformed state-of-the-art detection models for spark detection in terms of five metrics.