An efficient illuminance-reflectance nonlinear video stream enhancement model

Design of a systolic-pipelined architecture for real-time enhancement of color video stream based on an illuminance–reflectance model

Recently we proposed a wavelet-based dynamic range compression algorithm to improve the visual quality of digital images captured in the high dynamic range scenes with non-uniform lighting conditions. The fast image enhancement algorithm which provides dynamic range compression preserving the local contrast and tonal rendition is a very good candidate in aerial imagery applications such as image interpretation for defense and security tasks. This algorithm can further be applied to video streaming for aviation safety. In this paper the latest version of the proposed algorithm which is able to enhance aerial images so that the enhanced images are better then direct human observation, is presented. The results obtained by applying the algorithm to numerous aerial images show strong robustness and high image quality.

In this paper, we present a wireless video streaming system that securely and efficiently streams video to the heterogeneous clients within the home network, over time-varying communication links. We study the problem of secure and efficient video streaming over an IEEE 802.11e based wireless network and also address the issues concerning digital rights management (DRM). The importance of fine granularity scalability -moving picture experts group -4 (FGS-MPEG-4), a form of scalable video format, is discussed and its characteristics are exploited for efficient and secure video streaming. We have evaluated our proposed technique via NS2 based simulations and found that keeping the base layer encrypted and giving it a higher priority do not only fulfill the requirements of the DRM, but also help improve the overall quality of service (QoS). These findings are significant because they give the content providers a high level of security and also enhance the viewing experience of the mobile users.

The problem of estimation of video quality obtained by end-user for mobile video streaming is addressed. Widely spreading mobile communication systems and increasing data transmission rates expand variety of multimedia services. One of such services is video streaming. So it is important to assess quality of this service. Consumers of video streaming are humans, and quality assessment must account human perception characteristics. Existing methods for user experienced video quality estimation as quality metrics usually usebit-error rate that has low correlation with by human perceived video quality. More advanced methods usually require too much processing power that cannot be obtained in handled mobile devices or intrusion into device firmware and/or hardware to obtain required data. However, recent research shows that channels throughput dedicated to some service (e.g. video streaming) can be tied to QoS perceived by an end-user indicator. This paper presents a research on impact of wireless channel parameters such as throughput and jitter on quality of video streaming. These wireless channel parameters can be easily obtained by monitoring IP level data streams in end-user’s device by fairly simple software agent for indication of video streaming QoS. Ill. 5, bibl. 10 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.108.2.138

Video streams, either in form of on-demand streaming or live streaming, usually have to be converted (i.e., transcoded) based on the characteristics of clients' devices (e.g., spatial resolution, network bandwidth, and supported formats). Transcoding is a computationally expensive and time-consuming operation, therefore, streaming service providers currently store numerous transcoded versions of the same video to serve different types of client devices. Due to the expense of maintaining and upgrading storage and computing infrastructures, many streaming service providers (e.g., Netflix) recently are becoming reliant on cloud services. However, the challenge in utilizing cloud services for video transcoding is how to deploy cloud resources in a cost-efficient manner without any major impact on the quality of video streams. To address this challenge, in this paper, we present the Cloud-based Video Streaming Service (CVSS) architecture to transcode video streams in an on-demand manner. The architecture provides a platform for streaming service providers to utilize cloud resources in a cost-efficient manner and with respect to the Quality of Service (QoS) demands of video streams. In particular, the architecture includes a QoS-aware scheduling method to efficiently map video streams to cloud resources, and a cost-aware dynamic (i.e., elastic) resource provisioning policy that adapts the resource acquisition with respect to the video streaming QoS demands. Simulation results based on realistic cloud traces and with various workload conditions, demonstrate that the CVSS architecture can satisfy video streaming QoS demands and reduces the incurred cost of stream providers up to 70%.

Video streams, either in form of on-demand streaming or live streaming, usually have to be converted (i.e., transcoded) based on the characteristics (e.g., spatial resolution) of clients' devices. Transcoding is a computationally expensive operation, therefore, streaming service providers currently store numerous transcoded versions of the same video to serve different types of client devices. However, recent studies show that accessing video streams have a long tail distribution. That is, there are few popular videos that are frequently accessed while the majority of them are accessed infrequently. The idea we propose in this research is to transcode the infrequently accessed videos in a on-demand (i.e., lazy) manner. Due to the cost of maintaining infrastructure, streaming service providers (e.g., Netflix) are commonly using cloud services. However, the challenge in utilizing cloud services for video transcoding is how to deploy cloud resources in a cost-efficient manner without any major impact on the quality of video streams. To address the challenge, in this research, we present an architecture for on-demand transcoding of video streams. The architecture provides a platform for streaming service providers to utilize cloud resources in a cost-efficient manner and with respect to the Quality of Service (QoS) requirements of video streams. In particular, the architecture includes a QoS-aware scheduling component to efficiently map video streams to cloud resources, and a cost-efficient dynamic (i.e., elastic) resource provisioning policy that adapts the resource acquisition with respect to the video streaming QoS requirements.

The emerging 60-GHz multigigabits per second wireless technology enables the streaming of high-quality “uncompressed” video, which has been impossible with other existing wireless technologies. To support such a resource-hungry uncompressed video streaming service with limited wireless resources, it is necessary to design efficient link adaptation policies selecting suitable transmission rates for the 60-GHz wireless channel environment, thus optimizing video quality and resource management. For proper design of the link adaptation policies, we propose a new metric, called expected peak signal-to-noise ratio (ePSNR) , to numerically estimate the video streaming quality. By using the ePSNR as a criterion, we propose two link adaptation policies with different objectives considering unequal error protection (UEP). The proposed link adaptation policies attempt to 1) maximize the video quality for given wireless resources, or 2) minimize the required wireless resources, while meeting the video quality. From the link adaptation policies, we provide a distributed resource management scheme for multiple users to maintain satisfactory video streaming quality. Our extensive simulation results demonstrate that the newly proposed variable, i.e., ePSNR, well represents the level of video quality. It is also shown that the proposed link adaptation policies can enhance the resource efficiency while achieving acceptable quality of the video streaming.

The video streaming industry is growing. There is demand for high quality videos. These videos are stream to the consumers with a promising quality and low latency. There are various methods to measure the video quality of experience (QoE) in a streaming environment. The main goal of this paper is to provide an overview of methods and techniques to measure the QoE in adaptive streaming domain. This paper provide overview of metrics and QoE models which asses the video quality in streaming. This paper also discusses the dataset exist in video streaming. This paper highlights the challenges and future strategies that should be considered building models for assessing the video quality in adaptive streaming.

Over the past decade, video streaming on the Internet has become the primary source of our media consumption. Billions of users stream online video on multiple devices with an increasing expectation that video will be delivered at high quality without any rebuffering or other events that affect their Quality of Experience (QoE). Video streaming platforms leverage Content Delivery Networks (CDNs) to achieve this at scale. However, there is a gap in how the quality of video streams is monitored. Current solutions rely on client-side beacons that are issued actively by video players. While such approaches may be feasible for streaming platforms that deploy their own CDN, they are less applicable for third-party CDNs with multiple tenants and diverse video players.In this paper, we present a characterization of video workload from a global multi-tenant CDN and develop SSQoE: a methodology deployed on the server side which estimates rebuffering experienced by video clients using passive measurements. Using this approach, we calculate a QoE score which represents the health of a video stream across multiple consumers. We present our findings using this QoE score for various scenarios and compare it to traditional server and network monitoring metrics. We also demonstrate the QoE score’s efficacy during large streaming events such as the 2020 Superbowl LIV. We show that this server-side QoE estimation methodology is able to track video performance at an AS or user agent level and can easily pinpoint regional issues at the CDN, making it an attractive solution to be explored by researchers and other CDNs.

Path and server diversities have been used to guarantee reliable video streaming communication over wireless networks. In this paper, server diversity over mobile wireless ad hoc networks (MANETs) is implemented. Particularly, multipoint-to-point transmission together with multiple description coding (MDC) and forward error correction (FEC) technique is used to enhance the quality of service of video streaming over the wireless lossy networks. Additionally, the dynamic source routing (DSR) protocol is used to discover maximally disjoint routes for each sender and to distribute the workload evenly within the MANETs for video streaming applications. NS-2 Simulation study demonstrates the feasibility of the proposed mechanism and it shows that the approach achieves better quality of video streaming, in terms of the playable frame rate, reliability and real-time performance on the receiving side.

Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education, and entertainment. However, when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified into video attributes groups. The level of VCS streaming will gradually decrease to consider the least video selection that users will not accept depending on video quality. To evaluate the satisfaction level, we used the Mean Opinion Score (MOS) to measure the adaptability of user acceptance towards video streaming quality. The final results show that the proposed algorithm shows that the user satisfies the video selection, by altering the video attributes.

The wireless environment is built everywhere, especially IEEE 802.11 WLANs. More users use wireless network for searching information in Internets, TV program streaming, and video/voice conference. The packets include not only traditional data but also interactive multimedia data. There is an extension to IEEE 802.11 standards called IEEE 802.11e, improving the QoS capabilities on WLANs. This protocol enhances QoS data as above applications and features. The contribution, Cross-Layer Adaptive Video Prioritization (CAVP), in this thesis paper is divided into two parts. First, it provides MAC-Layer adaptive prioritization (MAP) to prioritize the packet which has the information of video frames at the Medium Access Control (MAC) Layer of IEEE 802.11e. In IEEE 802.11e, the priority of packets is very important, and it affects the order of received packets and the probability of successfully-received packet. The method to prioritize packets is estimating the access time of each access category (AC) for the arrived packet and putting the packet into the AC which has the minimal access time. Second, Video Frame Prioritization (VFP) prioritizes the video packet. Because the importance of video frames is different, we set the initial priority at Application Layer by choosing the maximal PSNR-difference frame as the highest priority frame. The maximal PSNR-difference frame critically influences the overall video sequences. These two parts of our contribution can greatly improve the quality of video streaming. The cross-layer design, CAVP, involves MAC Layer and Application Layer. The information of a packet arrived at MAC Layer is gotten from Application Layer. The benefit of cross-layer design is effectively using the information of different layers and combining their feature. In simulations, we use the popular simulator ns-2 as the experiment platform. We design several scenarios to show that our cross-layer design improves the quality of video streaming. The result shows that our cross-layer design proposal not only greatly increases PSNR of video but also improves the video in vision after the wireless transmission.

The HSUPA technology application to video streaming from moving vehicle to the central server is presented in the paper. A dedicated software for transmission control in case of non public IP address is employed. Quality of video streaming in urban area was measured. Several car routes were investigated in the area of the Polish Tricity. Measurements pointed out that the real time streaming quality during vehicle movement is sufficient within the prevailing time period.

Even though digital systems have many advantages over traditional analogue systems, end users expect that they will not loose any functionality in the transition. Concretely, capabilities of video cassette recorders (VCR) should be ported to stand alone digital media players (e.g., DVD players) and streaming services (e.g., digital TV and VoD services). Those capabilities are usually known as interactive playout or, simply, digital VCR functionality. Typical VCR features are random access, pause/resume, reverse play, fast forward/ backward, and slow motion. Random access and pause/resume functionalities are relatively easy to implement in digital systems. On the other hand, video coding techniques and bandwidth restrictions severely complicate the implementation of the other VCR operations. Usually, VCR capabilities apply only to video streams. In interactive playout mode, audio streams are commonly discarded. Also, the quality of video streams in interactive mode may be downgraded due to system limitations.

Multi-hop wireless networks (MWNs) provide an important infrastructure for ubiquitous multimedia content access. However, the accumulated packet losses greatly decrease the quality of multimedia services, particularly video streaming services. In order to overcome packet losses in MWNs, in this paper we propose a proportional integral derivative control-based forward error correction (PID-FEC) mechanism to improve the quality of video streaming services. Our proposed method adopts the cross-layer approach, and leverages the functionalities of different network layers. The automatic repeat request (ARQ) on the media access control (MAC) layer is used as an indicator of packet losses. With the packet loss information, the redundancy rates are adaptively regulated based on the PID control algorithm. Experimental results, presented herein, show that the PID-FEC achieves a better quality of video streaming, as well as a higher FEC efficiency, as compared with conventional FEC schemes, over a variety of network conditions.