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We present a method for fine grain QoS control of multimedia applications. This method takes as input an application software composed of actions. The execution times are unknown increasing functions of quality level parameters. Our method allows the construction of a quality manager which computes adequate action quality levels, so as to meet QoS requirements for a given platform. These include requirements for safety (action deadlines are met) as well as optimality (maximization and smoothness of quality levels). In this paper, we use learning techniques for computation of quality management policies. Given input parameters of the actions, a neural network is used to refine online pre-computed average execution times. Using refine average execution times allows a better control of the application, which leads to a reduction of fluctuations of CPU load. We present experimental results including the implementation of the method and benchmarks for an MPEG4 video encoder.