Digital Video Concepts, Methods, and Metrics: Quality, Compression, Performance, and Power Trade-off Analysis

Digital Video Concepts, Methods, and Metrics: Quality, Compression, Performance, and Power Trade-off Analysis is a concise reference for professionals in a wide range of applications and vocations. It focuses on giving the reader mastery over the concepts, methods and metrics of digital video coding, so that readers have sufficient understanding to choose and tune coding parameters for optimum results that would suit their particular needs for quality, compression, speed and power. The practical aspects are many: Uploading video to the Internet is only the beginning of a trend where a consumer controls video quality and speed by trading off various other factors. Open source and proprietary applications such as video e-mail, private party content generation, editing and archiving, and cloud asset management would give further control to the end-user. Digital video is frequently compressed and coded for easier storage and transmission. This process involves visual quality loss due to typical data compression techniques and requires use of high performance computing systems. A careful balance between the amount of compression, the visual quality loss and the coding speed is necessary to keep the total system cost down, while delivering a good user experience for various video applications. At the same time, power consumption optimizations are also essential to get the job done on inexpensive consumer platforms. Trade-offs can be made among these factors, and relevant considerations are particularly important in resource-constrained low power devices. To better understand the trade-offs this book discusses a comprehensive set of engineering principles, strategies, methods and metrics. It also exposes readers to approaches on how to differentiate and rank video coding solutions

Digital video concepts, methods, and metrics: quality, compression, performance and power trade-off analysis

Digital Video Concepts, Methods, and Metrics: Quality, Compression, Performance, and Power Trade-off Analysis is a concise reference for professionals in a wide range of applications and vocations. It focuses on giving the reader mastery over the concepts, methods and metrics of digital video coding, so that readers have sufficient understanding to choose and tune coding parameters for optimum results that would suit their particular needs for quality, compression, speed and power.The practical aspects are many: Uploading video to the Internet is only the beginning of a trend where a consumer controls video quality and speed by trading off various other factors. Open source and proprietary applications such as video e-mail, private party content generation, editing and archiving, and cloud asset management would give further control to the end-user.Digital video is frequently compressed and coded for easier storage and transmission. This process involves visual quality loss due to typical data compression techniques and requires use of high performance computing systems. A careful balance between the amount of compression, the visual quality loss and the coding speed is necessary to keep the total system cost down, while delivering a good user experience for various video applications. At the same time, power consumption optimizations are also essential to get the job done on inexpensive consumer platforms.Trade-offs can be made among these factors, and relevant considerations are particularly important in resource-constrained low power devices. To better understand the trade-offs this book discusses a comprehensive set of engineering principles, strategies, methods and metrics. It also exposes readers to approaches on how to differentiate and rank video coding solutions

Digital Video Concepts, Methods, and Metrics: Quality, Compression, Performance, and Power Trade-off Analysis

Computer scienc

Digital Video Concepts, Methods, and Metrics: Quality, Compression, Performance, and Power Trade-off Analysis

Computer scienc

1.1 Keywords Scheduling I/O for Parallel Video Encoding on a Cluster of Workstations

In this paper, we discuss the issues involved in I/ O for a parallel MPEG-2 video encoder on a cluster of workstations. Our goal is to achieve maximum performance of the system, that is, to minimize the finish time of the processors. We analyze the behavior of different models of the system and propose four I/O scheduling schemes to remove the I/O bottleneck. Our parallel video encoder uses a group of pictures as the basic unit of data to maintain data independence among computing processors. We present experimental results, showing the speedup of various I/O schemes. The performance of the parallel encoder is compared against a sequential counterpart

Real-time MPEG-2 video encoding on parallel and distributed systems

The information technology of future is likely to be highly dependent on digital video. The key obstacle in realizing this technology, however, lies in the transmission, access and storage requirements of huge amount of data. Compression of a video sequence is an inevitable solution to overcome this obstacle. While various video compression standards have been proposed, their software implementations pose a formidable challenge due to their great deal of processing requirements. On the other hand, the hardware solutions may not be cost-effective. This thesis deals with parallel implementation of the MPEG-2 video encoder on various parallel and distributed platforms. We use a data-parallel approach and exploit parallelism within each frame, unlike some of the previous approaches that employ multiple processing of several disjoint video sequences. This makes our encoder suitable for real-time applications where the complete video sequence may not be present on the disk and may become available on a frame-by-frame basis with time. The encoder also provides control over various parameters, and has the flexibility to allow the inclusion of various algorithms for different stages of the codec. An encoding rate higher than 30 frames/set has been achieved on the Intel Paragon. The encoder is portable across various platforms and allows the user to control the granularity of the problem by enabling it to run on a few fast workstations in a coarse-grained fashion as well as on large scale massively parallel processors in a fine-grained fashion