Polygonal meshes are popular three-dimensional virtual representations employed in a wide range of applications. Users have very high expectations with respect to the accuracy of these virtual representations, fueling a steady increase in the processing power and performance of graphics processing hardware. This accuracy is closely related to how detailed the virtual representations are. The more detailed these representations become, the higher the amount of data that will need to be displayed, stored, or transmitted. Efficient compression techniques are of critical importance in this context. State-ofthe-art compression performance of semi-regular mesh coding systems has been achieved through the use of subdivision-based wavelet coding techniques. However, the vast majority of these codecs are optimized with respect to the L2distortion metric, i.e., the average error. This makes them unsuitable for applications where each input signal sample has a certain significance. To alleviate this problem, we propose to optimize the mesh codec with respect to the L∞metric, which allows for the control of the local reconstruction error. This paper proposes novel data-dependent formulations for the L∞distortion. The proposed L∞estimators are incorporated in a state-of-the-art wavelet-based semi-regular mesh codec. The resulting coding system offers scalability in L∞sense. The experiments demonstrate the advantages of L∞coding in providing a tight control on the local reconstruction error. Furthermore, the proposed data-dependent L∞approaches significantly improve estimation accuracy, reducing the classical low-rate gap between the estimated and actual L∞distortion observed for previous L∞estimators.

Computation-intensive image processing applications need to be implemented on multicore architectures. If they are to be executed efficiently on such platforms, the underlying data and/or functions should be partitioned and distributed among the processors. The optimal partitioning approach is the one which aims to minimize the inter-processor communication while maximizing the load balance. With the continuously increasing number of cores which exacerbates the demand for more complex memory hierarchies, non-uniform memory access, etc., on-chip communication has gained a significant role in taking advantage of the multicore chips. Therefore, making partitioning decisions just based on conventional performance results and without communication profiling is suboptimal. In this paper, we explore the behavior of a mesh decoder as a case study in terms of communication and computation, and propose models that allow early prediction of the application's behavior. Using these models, profiling the application for all of the input samples is not necessary anymore. As a result, communication- and computation-aware parallelization could be performed faster and easier.