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- Light field image Coding using high-order intrablock predictionPublication . Monteiro, Ricardo J. S.; Nunes, Paulo J. L.; Rodrigues, Nuno M. M.; Faria, Sérgio M. M. deThis paper proposes a two-stage high-order intrablock prediction method for light field image coding. This method exploits the spatial redundancy in lenslet light field images by predicting each image block, through a geometric transformation applied to a region of the causal encoded area. Light field images comprise an array of microimages that are related by complex geometric transformations that cannot be efficiently compensated by state-of-the-art image coding techniques, which are usually based on low-order translational prediction models. The two-stage nature of the proposed method allows us to choose the order of the prediction model most suitable for each block, ranging from pure translations to projective or bilinear transformations, optimized according to an appropriate rate-distortion criterion. The proposed higher order intrablock prediction approach was integrated into a high efficiency video coding (HEVC) codec and evaluated for both unfocused and focused light field camera models, using different resolutions and microlens arrays. Experimental results show consistent bitrate savings, which can go up to 12.62%, when compared to a lower order intrablock prediction solution and 49.82% when compared to HEVC still picture coding.
- Deep Learning-Based Point Cloud Coding and Super-Resolution: a Joint Geometry and Color ApproachPublication . Guarda, André F. R.; Ruivo, Manuel; Coelho, Luís; Seleem, Abdelrahman; M. M. Rodrigues, Nuno; Pereira, FernandoIn this golden age of multimedia, realistic content is in high demand with users seeking more immersive and interactive experiences. As a result, new image modalities for 3D representations have emerged in recent years, among which point clouds have deserved especial attention. Naturally, with this increase in demand, efficient storage and transmission became a must, with standardization groups such as MPEG and JPEG entering the scene, as it happened before with other types of visual media. In a surprising development, JPEG issued a Call for Proposals on point cloud coding targeting exclusively learningbased solutions, in parallel to a similar call for image coding. This is a natural consequence of the growing popularity of deep learning, which due to its excellent performances is currently dominant in the multimedia processing field, including coding. This paper presents the coding solution selected by JPEG as the best-performing response to the Call for Proposals and adopted as the first version of the JPEG Pleno Point Cloud Coding Verification Model, in practice the first step for developing a standard. The proposed solution offers a novel joint geometry and color approach for point cloud coding, in which a single deep learning model processes both geometry and color simultaneously. To maximize the RD performance for a large range of point clouds, the proposed solution uses down-sampling and learningbased super-resolution as pre- and post-processing steps. Compared to the MPEG point cloud coding standards, the proposed coding solution comfortably outperforms G-PCC, for both geometry, color, and joint quality metrics.
- Codificação de Vídeo utilizando Transformações Geométricas e de LuminânciaPublication . Rodrigues, Nuno Miguel MoraisNesta tese estudamos a aplicação de transformações geométricas no domínio da luminância na codificacão de sinais de vídeo digital. As transformações geométricas tem vindo a ser utilizadas na estimação e compensação de movimento pois permitem a representação de um maior número de situações, relativamente aos métodos clássicos baseados apenas em vectores de movimento de translação. No entanto, a utilização de transformações geométricas espaciais apresenta algumas dificuldades na compensação de fenómenos relacionados com: a exposição de objectos da cena que anteriormente se encontravam encobertos; o aparecimento de novos objectos; e as mudanças causadas pela alteração das condições de iluminação. E proposta a utilização de um novo tipo de transformações, realizadas no domínio da luminância, de modo a permitir uma compensação mais eficiente dos 1’enómenos referidos. Após uma introdução ao problema genérico da codificação de sequencias e da utilização de transformações geométricas na estimação e compensação de movimento, ó apresentado o estudo das novas transformações no domínio da luminância. Duas novas técnicas de estimação e compensação de movimento foram desenvolvidas, que combinar as transformações de luminância com as técnicas baseadas em vectores de translação e em transforrmacões geométricas. As novas técnicas foram testadas em sistemas de codificação de vídeo digital com baixos débitos. Os resultados obtidos nestes testes foram avaliados e comparados com os dos métodos tradicionais, tendo-se verificado que a sua uti1izacão permite uma melhoria significativa da eficiência da estimação e compensação de movimento. Foi também estudada a aplicação das novas técnicas desenvolvidas na codificação de sequências estereoscópicas.
- On ECG Signal Compression With 1-D Multiscale Recurrent Patterns Allied to Preprocessing TechniquesPublication . Filho, E.B.L.; Rodrigues, N.M.M.; Silva, E.A.B. da; Carvalho, M.B. de; Faria, S.M.M. de; Silva, V.M.M. da; M. M. Rodrigues, Nuno; Faria, SergioThis paper presents the results of a multiscale pattern-matching-based ECG encoder, which employs simple preprocessing techniques for adapting the input signal. Experiments carried out with records from the Massachusetts Institute of Technology-Beth Israel Hospital database show that the proposed scheme is effective, outperforming some state-of-the-art schemes described in the literature.
- Improving multiscale recurrent pattern image coding with least-squares prediction modePublication . Graziosi, Danillo B.; Rodrigues, Nuno M. M.; Silva, Eduardo A. B. da; Faria, Sérgio M. M. de; Carvalho, Murilo B. de; Faria, Sergio; M. M. Rodrigues, Nuno;The Multidimensional Multiscale Parser-based (MMP) image coding algorithm, when combined with flexible partitioning and predictive coding techniques (MMP-FP), provides state-of-the-art performance. In this paper we investigate the use of adaptive least-squares prediction in MMP. The linear prediction coefficients implicitly embed the local texture characteristics, and are computed based on a block’s causal neighborhood (composed of already reconstructed data). Thus, the intra prediction mode is adaptively adjusted according to the local context and no extra overhead is needed for signaling the coefficients. We add this new context-adaptive linear prediction mode to the other MMP prediction modes, that are based on the ones used in H.264/AVC; the best mode is chosen through rate-distortion optimization. Simulation results show that least-squares prediction is able to significantly increase MMP-FPs rate-distortion performance for smooth images, leading to better results than the ones of state-of-theart, transform-based methods. Yet with the addition of least-squares prediction MMP-FP presents no performance loss when used for encoding non-smooth images, such as text and graphics.
- Improving Point Cloud to Surface Reconstruction with Generalized Tikhonov RegularizationPublication . Guarda, André; Bioucas-Dias, José M.; M. M. Rodrigues, Nuno; Pereira, Fernando; Bernardo Pereira, Fernando ManuelPoint cloud rendering has a vital role in the user Quality of Experience for applications adopting point cloud based representations. While this is not a new area, it has recently become more relevant with the recent interest on point cloud coding by major standardization groups, notably JPEG and MPEG. The screened Poisson surface reconstruction is a state-ofthe- art technique for generating a watertight surface mesh from the point cloud samples. While its screening component allows the surface to better fit the cloud points, this fitting may lead to undesired artifacts in the surface, notably when the point cloud is noisy. This paper proposes to improve this reconstruction method by making it more robust to noise by adopting a generalized Tikhonov regularization term. The proposed regularization approach smooths regions that should be flat while keeping the important details in the edges, thus creating more pleasant surface reconstructions.
- Point Cloud Coding: Adopting a Deep Learningbased ApproachPublication . Guarda, André; M. M. Rodrigues, Nuno; Pereira, FernandoPoint clouds have recently become an important visual representation format, especially for virtual and augmented reality applications, thus making point cloud coding a very hot research topic. Deep learning-based coding methods have recently emerged in the field of image coding with increasing success. These coding solutions take advantage of the ability of convolutional neural networks to extract adaptive features from the images to create a latent representation that can be efficiently coded. In this context, this paper extends the deep-learning coding approach to point cloud coding using an autoencoder network design. Performance results are very promising, showing improvements over the Point Cloud Library codec often taken as benchmark, thus suggesting a significant margin of evolution for this new point cloud coding paradigm.
- Light field HEVC-based image coding using locally linear embedding and self-similarity compensated predictionPublication . Monteiro, Ricardo; Lucas, Luis; Conti, Caroline; Nunes, Paulo; M. M. Rodrigues, Nuno; Faria, Sergio; Pagliari, Carla; Silva, Eduardo da; Soares, LuisLight field imaging is a promising new technology that allows the user not only to change the focus and perspective after taking a picture, as well as to generate 3D content, among other applications. However, light field images are characterized by large amounts of data and there is a lack of coding tools to efficiently encode this type of content. Therefore, this paper proposes the addition of two new prediction tools to the HEVC framework, to improve its coding efficiency. The first tool is based on the local linear embedding-based prediction and the second one is based on the self-similarity compensated prediction. Experimental results show improvements over JPEG and HEVC in terms of average bitrate savings of 71.44% and 31.87%, and average PSNR gains of 4.73dB and 0.89dB, respectively.
- Image Coding Using Generalized Predictors Based on Sparsity and Geometric TransformationsPublication . Lucas, Luís F. R.; M. M. Rodrigues, Nuno; Silva, Eduardo A. B. da; Pagliari, Carla L.; Faria, Sergio
- Recurrent pattern matching based stereo image coding using linear predictorsPublication . Lucas, Luís F. R.; M. M. Rodrigues, Nuno; Pagliari, Carla L.; Silva, Eduardo A. B. da; Faria, Sérgio M. M. deThe Multidimensional Multiscale Parser (MMP) is a pattern-matching-based generic image encoding solution which has been investigated earlier for the compression of stereo images with successful results. While first MMP-based proposals for stereo image coding employed dictionary-based techniques for disparity compensation, posterior developments have demonstrated the advantage of using predictive methods. In this paper, we focus on recent investigations on the use of predictive methods in the MMP algorithm and propose a new prediction framework for efficient stereo image coding. This framework comprises an advanced intra directional prediction model and a new linear predictive scheme for efficient disparity compensation. The linear prediction model is the main novelty of this work, combining adaptive linear models estimated by least-squares algorithm with fixed linear models provided by the block-matching algorithm. The performance of the proposed intra prediction and disparity compensation methods when applied in an MMP encoder has been evaluated experimentally. Comparisons with the current stereo image coding standards showed that the proposed MMP algorithm significantly outperforms the Stereo High Profile of H.264/AVC standard. In addition, it presents a competitive performance relative to the MV-HEVC standard. These results also suggest that current stereo image coding standards may benefit from the proposed linear prediction scheme for disparity compensation, as an extension to the omnipresent block-matching solution.