Computation of the minimum data storage and applications in memory management for multimedia signal processing

Abstract

Abstract. The amount of the data storage in signal processing systems, whose behavior is described by loop-organized algorithmic specifications, has an important impact on the overall energy consumption, chip area, as well as system performance. This paper presents a methodology based on lattices [25] which can be used to address several memory management tasks for applications with high-level specifications, where the main data structures are multidimensional arrays. This methodology was used in the past for the exact computation of the minimum data storage in applications with procedural, affine specifications [2]. The paper discusses two applications of that technique in the memory management of data-dominated signal processing systems: (1) the evaluation of the impact of loop transformations on the data storage, and (2) the assessment and efficient implementation of models of mapping multidimensional signals into the physical memory.

An efficient fingerprint image compression technique based on wave atoms decomposition and multistage vector quantization

“…Wavelet transform has been profusely used to address problems in; signal and speech processing [32,33,62,67]; data compression [23][24][25]61], pattern recognition [27][28][29]31,34,36,39,57,60], image reconstruction [55], and biomedical engineering applications [46,48,51,65]. Wavelets have ability to better represent objects with point singularities in 1D and 2D space but fail to deal with singularities along curves in 2D.…”