Browsing by Author "Lucas, Luis F. R."
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- Intra Predictive Depth Map Coding Using Flexible Block PartitioningPublication . Lucas, Luis F. R.; Wegner, Krzysztof; M. M. Rodrigues, Nuno; Pagliari, Carla L.; Silva, Eduardo A. B. da; Faria, SergioA complete encoding solution for efficient intra-based depth map compression is proposed in this paper. The algorithm, denominated predictive depth coding (PDC), was specifically developed to efficiently represent the characteristics of depth maps, mostly composed by smooth areas delimited by sharp edges. At its core, PDC involves a directional intra prediction framework and a straightforward residue coding method, combined with an optimized flexible block partitioning scheme. In order to improve the algorithm in the presence of depth edges that cannot be efficiently predicted by the directional modes, a constrained depth modeling mode, based on explicit edge representation, was developed. For residue coding, a simple and low complexity approach was investigated, using constant and linear residue modeling, depending on the prediction mode. The performance of the proposed intra depth map coding approach was evaluated based on the quality of the synthesized views using the encoded depth maps and original texture views. The experimental tests based on all intra configuration demonstrated the superior rate-distortion performance of PDC, with average bitrate savings of 6%, when compared with the current state-of-the-art intra depth map coding solution present in the 3D extension of a high-efficiency video coding (3D-HEVC) standard. By using view synthesis optimization in both PDC and 3D-HEVC encoders, the average bitrate savings increase to 14.3%. This suggests that the proposed method, without using transform-based residue coding, is an efficient alternative to the current 3D-HEVC algorithm for intra depth map coding.
- Lossless Compression of Medical Images Using 3-D PredictorsPublication . Lucas, Luis F. R.; M. M. Rodrigues, Nuno; Cruz, Luis A. da Silva; Faria, Sergio M. M. deThispaper describes a highly efficientmethod for lossless compression of volumetric sets of medical images, such as CTs or MRIs. The proposed method, referred to as 3-D-MRP, is based on the principle ofminimum rate predictors (MRPs), which is one of the state-of-the-art lossless compression technologies presented in the data compression literature. The main features of the proposed method include the use of 3-D predictors, 3-D-block octree partitioning and classification, volume-based optimization, and support for 16-b-depth images. Experimental results demonstrate the efficiency of the 3-D-MRP algorithm for the compression of volumetric sets of medical images, achieving gains above 15% and 12% for 8- and 16-bitdepth contents, respectively, when compared with JPEGLS, JPEG2000, CALIC, and HEVC, aswell as other proposals based on the MRP algorithm.
- Sparse least-squares prediction for intra image codingPublication . Lucas, Luis F. R.; M. M. Rodrigues, Nuno; Pagliari, Carla L.; Silva, Eduardo A. B. da; Faria, Sergio M. M. deThis paper presents a new intra prediction method for efficient image coding, based on linear prediction and sparse representation concepts, denominated sparse least-squares prediction (SLSP). The proposed method uses a low order linear approximation model which may be built inside a predefined large causal region. The high flexibility of the SLSP filter context allows the inclusion of more significant image features into the model for better prediction results. Experiments using an implementation of the proposed method in the state-of-the-art H.265/HEVC algorithm have shown that SLSP is able to improve the coding performance, specially in the presence of complex textures, achieving higher coding gains than other existing intra linear prediction methods. © 2015 IEEE.