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Existing methods treat computing quality as the number of clock cycles to execute a task, but they overlook the fact that the quality of many real-life applications exhibit the characteristic of diminishing return as the processor continues executing. The diminishing return of the quality is largely due to the features of iterative processing or successive refinement inherent in those applications. Ignoring it leads to large over-estimation in contemporary quality optimization approaches. In this article, we exploit the application adaptability to achieve quality maximization by taking both system resource constraints and diminishing return of the quality into account. We first reveal that the diminishing return of the quality is inherent in several well-known applications, and suggest an exponential model that accurately captures it. Second, we propose a dynamic frequency scaling (DFS) methodology to optimally decide the processor execution cycles for such applications, in order to maximize the output quality under system energy, timing, and temperature constraints. We transform the DFS problem to an iterative pseudo quadratic programming heuristic that can be efficiently solved. Third, we present a wrapping dynamic voltage scaling (wDVS) methodology to achieve further quality improvement, by judiciously adjusting the supply voltage to provide extra frequency scaling space. 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