Bandwidth Estimation and Optimized Bitrate Selection for Dynamic Adaptive Streaming Over Http Using RSI-GM And ISSO

Dynamic Adaptive Streaming over HTTP (DASH) is an emerging solution that aims to standardize existing proprietary streaming systems. DASH specification defines the media presentation description (MPD), which describes a list of available content, URL addresses, and the segment format. High bandwidth demands in interactive streaming applications pose challenges in efficiently utilizing the available bandwidth. In this paper, a novel Relative Strength Index (RSI) with Geometric mean (GM) namely RSI-GM is proposed for estimating available bandwidth for DASH. The proposed work starts by taking the video as an input at the transmitter side and then the video compression is performed using the TRLE. Then MD5 hashing-based AES encryption is applied to the compressed video data to provide data security. Then RSI-GM is proposed to estimate the available bandwidth for DASH. Finally, after estimation, the bitrate for estimated bandwidth is selected optimally using the Improved Shark Smell Optimization (ISSO) algorithm.

Bleeding Pixels

This chapter examines the aesthetic properties and phenomenological effects of compression glitches—blocky image distortions that momentarily deform digitally compressed video. As visible expressions of the invisible processes of digital video compression, compression glitches offer unprecedented encounters with the technological production of cinematic motion. Two distinct consequences of these encounters are explored in this chapter. First, because compression glitches are more likely to occur when the compression algorithm is overworked by large volumes of onscreen movement, the ubiquity of compression glitches has yielded a spectatorial sensitivity to the magnitude of movement on screen. Second, because compression glitches extract movement itself (i.e., algorithmic motion instructions) from its original visual context, the visual qualities of such glitches heighten our attention to the formal qualities of movement as distinct from the actions and events that such movements comprise. Taken together, these two spectatorial effects of the compression glitch illuminate new orientations toward cinematic motion in the digital era. Describing these orientations, the chapter argues, can model a form of inquiry that bridges the gap between technologically oriented and phenomenologically oriented accounts of “digital cinema.”

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.

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.

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.

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.