Luis F. R. Lucas

Light field HEVC-based image coding using locally linear embedding and self-similarity compensated prediction

Light 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.

Intra-prediction for color image coding using YUV correlation

In this paper we present a new algorithm for chroma prediction in YUV images, based on inter component correlation. Despite the YUV color space transformation for inter component decorrelation, some dependency still exists between the Y, U and V chroma components. This dependency has been previously used to predict the chrominance data from the reconstructed luminance. In this paper we show that a chrominance component can be more efficiently predicted by using the reconstructed data from both the luminance and the remaining chrominance signal. The proposed chroma prediction is implemented and tested using the Multidimensional Multiscale Parser (MMP) image encoding algorithm. It is shown that the new color prediction mode outperforms the originally proposed prediction methods. Furthermore, by using the new color prediction scheme, MMP is consistently better than the state-of-the-art H.264/AVC for coding both for the luminance and the chrominance image components.

Intra Predictive Depth Map Coding Using Flexible Block Partitioning

A 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.

Predictive depth map coding for efficient virtual view synthesis

This paper presents a novel approach to compress depth maps envisioned for virtual view synthesis. This proposal uses a sophisticated prediction model, combining the HEVC intra prediction modes with a flexible partitioning scheme. It exhaustively evaluates the prediction modes for a large amount of block sizes, in order to find the minimum coding cost for each depth map block. Unlike HEVC, no transform is used, the residue being trivially encoded through the transmission of just its mean value. The experimental results show that, when the encoding evaluation metric is the quality of the view synthesized using the encoded depth map against the map encoding rate, the proposed algorithm generates reconstructed depth maps that provide, for most bitrates, some of the best performances among state-of-the-art depth maps encoders. In addition, it runs approximately as fast as the HEVC HM.

Efficient depth map coding using linear residue approximation and a flexible prediction framework

The importance to develop more efficient 3D and multiview data representation algorithms results from the recent market growth for 3D video equipments and associated services. One of the most investigated formats is video+depth which uses depth image based rendering (DIBR) to combine the information of texture and depth, in order to create an arbitrary number of views in the decoder. Such approach requires that depth information must be accurately encoded. However, methods usually employed to encode texture do not seem to be suitable for depth map coding. In this paper we propose a novel depth map coding algorithm based on the assumption that depth images are piecewise-linear smooth signals. This algorithm is designed to encode sharp edges using a flexible dyadic block segmentation and hierarchical intra-prediction framework. The residual signal from this operation is aggregated into blocks which are approximated using linear modeling functions. Furthermore, the proposed algorithm uses a dictionary that increases the coding efficiency for previously used approximations. Experimental results for depth map coding show that synthesized views using the depth maps encoded by the proposed algorithm present higher PSNR than their counterparts, demonstrating the methods efficiency.

Stereo image coding using dynamic template-matching prediction

Template matching (TM) has been originally proposed as a texture synthesis tool. However, it has been successfully exploited for spatial and inter-frame prediction in video coding. In this paper we investigate the use of TM prediction for stereo image coding. In order to efficiently encode stereo images, the TM algorithm was optimized for stereo disparity prediction. Additionally, we have used the state-of-the-art pattern matching based algorithm, the Multidimensional Multiscale Parser (MMP), to encode images predicted with the proposed scheme. Experimental results of the developed stereo image encoder show that TM is able to efficiently exploit the redundancy between stereo views. The use of the new prediction method with MMP also achieves important coding gains over the state-of-the-art transform based H.264/AVC standard, for stereo image coding.

Sparse least-squares prediction for intra image coding

This 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.

Adaptive least squares prediction for stereo image coding

State-of-the art approaches towards stereo image coding exploit inter-view redundancy by employing block-matching methods for disparity estimation and compensation. However, the efficiency of these methods is affected by mismatched areas, due to occlusions, brightness variations, or perspective distortion between objects of the two views. In this paper we present a new prediction scheme for stereo image coding, that combines an implicit disparity estimation method, with an adaptive least squares (LS)-based filtering. The Multidimensional Multiscale Parser image coding algorithm was used to evaluate the efficiency of the proposed scheme. Experimental results demonstrate the advantage of LS prediction in stereo image coding. Furthermore, the rate-distortion performance of the MMP based stereo encoder is well above that of the state-of-the-art H.264/AVC Stereo Profile, especially at medium and high bit rates.

Lossless Compression of Medical Images Using 3-D Predictors

This paper describes a highly efficient method 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 of minimum 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-bit-depth contents, respectively, when compared with JPEG-LS, JPEG2000, CALIC, and HEVC, as well as other proposals based on the MRP algorithm.

Image Coding Using Generalized Predictors Based on Sparsity and Geometric Transformations

Directional intra prediction plays an important role in current state-of-the-art video coding standards. In directional prediction, neighbouring samples are projected along a specific direction to predict a block of samples. Ultimately, each prediction mode can be regarded as a set of very simple linear predictors, a different one for each pixel of a block. Therefore, a natural question that arises is whether one could use the theory of linear prediction in order to generate intra prediction modes that provide increased coding efficiency. However, such an interpretation of each directional mode as a set of linear predictors is too poor to provide useful insights for their design. In this paper, we introduce an interpretation of directional prediction as a particular case of linear prediction, which uses the first-order linear filters and a set of geometric transformations. This interpretation motivated the proposal of a generalized intra prediction framework, whereby the first-order linear filters are replaced by adaptive linear filters with sparsity constraints. In this context, we investigate the use of efficient sparse linear models, adaptively estimated for each block through the use of different algorithms, such as matching pursuit, least angle regression, least absolute shrinkage and selection operator, or elastic net. The proposed intra prediction framework was implemented and evaluated within the state-of-the-art high efficiency video coding standard. Experiments demonstrated the advantage of this predictive solution, mainly in the presence of images with complex features and textured areas, achieving higher average bitrate savings than other related sparse representation methods proposed in the literature.