Browsing by Author "Falcao, Gabriel"
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- Energy-Efficient and Portable Least Squares Prediction for Image Coding on a Mobile GPUPublication . Cordeiro, Pedro; Falcao, Gabriel; Domingues, Patrício; Rodrigues, Nuno; Faria, SergioLeast squares prediction is a technique used to foresee pixel values during image coding by finding the minimum square error of neighbouring pixels. It has shown considerable quality gains especially for complex images with high variations in pixel intensities. The drawback of this technique consists of high computational complexity, consuming the most significant part of processing time and resources available, which makes it difficult to implement in fast, lossy image coders. One challenge is therefore to reduce the computational time of this predictor, namely through the use of new parallel programming techniques, making it more attractive for state-of-the-art coder-decoders. Also, new algorithmic propositions are made, trying to reduce the time spent in exchange for rate-distortion performance. These propositions are senseful since this predictor is used not only in lossless image coding, but also in lossy as well. Another aim of this article is to analyze energy efficiency among different types of platforms for this signal processing algorithm. Comparisons are provided on parallel computing processors ranging from very powerful Graphics Computing Units (GPUs) to mobile General-Purpose GPUs.
- Optimized fast Walsh-Hadamard transform on OpenCL-GPU and OpenCL-CPUPublication . Pereira, Pedro M. M.; Domingues, Patrício; M. M. Rodrigues, Nuno; Faria, Sergio M. M. de; Falcao, GabrielThe Walsh-Hadamard transform plays a major role in many image and video coding algorithms. In one hand, its intensive use in these algorithms makes its acceleration a challenge, in order to speed-up the algorithm execution. On the other hand, the available fast implementations are not efficient across different platforms. In this work, a parallel-based implementation of the WHT is proposed for CPU and GPU platforms using the OpenCL standard. OpenCL achieves portability at code level, but its performance suffers when the same code is used for CPUs and GPUs. To achieve top performance, we propose two WHT versions: OpenCL-GPU for GPUs and OpenCL-CPU for CPUs. Broadly, OpenCL-GPU executed on a GPU runs faster than OpenCL-CPU executed on a multicore CPU, with speedups that range from 120.87 to 1016.35. However, OpenCL-GPU performance drops substantially when ran on a multicore CPU machine, where OpenCL-CPU achieves higher performance, as it exploits the OpenCL support for SIMD instructions.