Optimizing a Parallel Video Encoder with Message Passing and a Shared Memory Architecture

Implementing video applications on emerging multi-core processors is a promising technique for personal, real-time multi-media applications. However, when porting the legacy parallel video encoders developed for clusters to shared-memory multi-cores, the existing parallel algorithms result in workload imbalances on different cores and communication inefficiencies. This paper describes a strip-wise parallel scheme to balance workloads and a hybrid communication mechanism to reduce communication overhead. The implementation of the H.264 parallel encoder on an eight CPU Intel Xeon system achieves 5x to 6x speed-up over a single thread encoder and achieves a 29% performance improvement over the commonly used master-slave schemes on clusters. The paper also gives further analysis on scalability, parallel efficiency, workload balance, and communication overhead as the number of cores varies.

A VIDEO SPECTRUM SPLITTING ENCODING SCHEME BASED ON HUMAN VISION AND ITS COMPUTER SIMULATION

In this paper, a 3-D video encoding scheme suitable for digital TV/HDTV (high definition television) is studied through computer simulation. The encoding scheme is designed to provide a good match to human vision. Basically, this involves transmission of low frequency luminance information at full frame rate for good motion rendition and transmission of high frequency luminance signal at reduced frame rate for good detail in static images.

Bit Rate Reduction in Cloud Gaming Using Object Detection Technique

The past two decades witnessed a broad-increase in web technology and on-line gaming.Enhancing the broadband confinements is viewed as one of the most significant variables that prompted new gaming technology.The immense utilization of web applications and games additionally prompted growth in the handled devices and moving the limited gaming experience from user devices to online cloud servers.As internet capabilities are enhanced new ways of gaming are being used to improve the gaming experience.In cloud-based video gaming,game engines are hosted in cloud gaming data centers,and compressed gaming scenes are rendered to the players over the internet with updated controls.In such systems,the task of transferring games and video compression imposes huge computational complexity is required on cloud servers.The basic problems in cloud gaming in particular are high encoding time,latency,and low frame rates which require a new methodology for a better solution.To improve the bandwidth issue in cloud games,the compression of video sequences requires an alternative mechanism to improve gaming adaption without input delay.In this paper,the proposed improved methodology is used for automatic unnecessary scene detection,scene removing and bit rate reduction using an adaptive algorithm for object detection in a game scene.As a result,simulations showed without much impact on the players’quality experience,the selective object encoding method and object adaption technique decrease the network latency issue,reduce the game streaming bitrate at a remarkable scale on different games.The proposed algorithm was evaluated for three video game scenes.In this paper,achieved 14.6%decrease in encoding and 45.6%decrease in bit rate for the first video game scene.