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In this paper, we apply dynamic voltage scaling (DVS) to the matching metric computation (MMC) used within motion estimation (ME) in typical video encoders. Our approach is based on "soft DSP" concepts. We analyze the effect of ME errors (due to DVS) in overall coding performance. We propose a model for the resulting rate increase (at a given fixed quantization parameter) as a function of input characteristics and input voltage, for given ME algorithm and MMC architecture. This model is validated using simulations. We then compare ME algorithms and MMC architectures, and propose a method for power saving of the ME process that depend on input characteristics and desired coding performance. As an illustration of the potential benefits of allowing computation errors, we show that allowing errors that lead to a small rate increase (about 3%) produces 37% power savings in the ME process, as compared to not using DVS. An essentially "error-free" DVS approach (no rate penalty) can achieve around 10% power savings.

In this paper we study the computation error tolerance properties of motion estimation algorithms. We are motivated by two scenarios where hardware systems may introduce computation errors. First, we consider hardware faults such as those arising in a typical fabrication process. Second, we consider "soft" errors due to voltage scaling, which can arise when operating at a lower voltage than specified for the system. Current practice is to discard all faulty systems. However there is an increasing interest in tools that can identify faulty systems which provide acceptable performance. We show that motion estimation (ME) algorithms exhibit significant error tolerance in these two scenarios. We propose simple error models and use these to provide insights into what features in these ME algorithms lead to increased error tolerance. Our comparison of the full search ME and a state of the art fast ME approach in the context of H.264/AVC shows that while both techniques are error tolerant, the faster algorithm is in fact more robust to computation errors.