Complexity metric driven energy optimization framework for implementing MPEG-21 scalable video decoders

We propose a systematic framework to optimize the energy consumption of wavelet-based scalable video decoders using generic computational complexity metrics derived from the frequency of execution of program basic blocks (Aho, A. et al., "Compilers: Principles, Techniques and Tools", chap.9, p.528, Addison-Wesley, 1986). The complexity metrics are independent of the hardware architecture/resources of the decoders and capture both the time varying video content characteristics and the corresponding encoding parameters. We show how the generic complexity metrics can be translated into a platform-specific metric, such as the execution time, which in turn can guide the selection of optimal voltage and frequency selection to optimize the energy. Preliminary results show 67% to 83% improvements in energy consumption on key functions of the 3D wavelet based scalable codec (SVC), which is a likely candidate for the emerging MPEG-21 video standard (Ohm, J.R. et al., Image Communications, 2004). Most of the processing in the proposed framework is done off-line, and hence it has limited impact in terms of delay or additional resource requirements for the decoder.