Statistical framework for video decoding complexity modeling and prediction
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Development of a high-level simulation approach and its application to multicore video decoding
IEEE Transactions on Circuits and Systems for Video Technology
Profiling-based hardware/software co-exploration for the design of video coding architectures
IEEE Transactions on Circuits and Systems for Video Technology
Evaluation of data-parallel H.264 decoding approaches for strongly resource-restricted architectures
Multimedia Tools and Applications
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Image Communication
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An appropriate complexity analysis stage is the first and fundamental step for any methodology aiming at the implementation of today's (complex) multimedia algorithms. Such a stage may have different final implementation goals such as defining a new architecture dedicated to the specific multimedia standard under study, or defining an optimal instruction set for a selected processor architecture, or to guide the software optimization process in terms of control-flow and data-flow optimization targeting a specific architecture. The complexity of nowadays multimedia standards, in terms of number of lines of codes and cross-relations among processing algorithms that are activated by specific input signals, goes far beyond what the designer can reasonably grasp from the "pencil and paper" analysis of the (software) specifications. Moreover, depending on the implementation goal different measures and metrics are required at different steps of the implementation methodology or design flow. The process of extracting the desired measures needs to be supported by appropriate automatic tools, since code rewriting, at each design stage, may result resource consuming and error prone. This paper reviews the state of the art of complexity analysis methodologies oriented to the design of multimedia systems and presents an integrated tool for automatic analysis capable of producing complexity results based on rich and customizable metrics. The tool is based on a C virtual machine that allows extracting from any C program execution the operations and data-flow information, according to the defined metrics. The tool capabilities include the simulation of virtual memory architectures. This paper shows some examples of complexity analysis results that can be yielded with the tool and presents how the tools can be used at different stages of implementation methodologies.