RESUMEN

State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omnidirectional images have specific spatial and statistical properties that can not be fully captured by current CNN models. Second, basic mathematical operations composing a CNN architecture, e.g., translation and sampling, are not well-defined on the sphere. In this paper, we study the learning of representation models for omnidirectional images and propose to use the properties of HEALPix uniform sampling of the sphere to redefine the mathematical tools used in deep learning models for omnidirectional images. In particular, we: i) propose the definition of a new convolution operation on the sphere that keeps the high expressiveness and the low complexity of a classical 2D convolution; ii) adapt standard CNN techniques such as stride, iterative aggregation, and pixel shuffling to the spherical domain; and then iii) apply our new framework to the task of omnidirectional image compression. Our experiments show that our proposed on-the-sphere solution leads to a better compression gain that can save 13.7% of the bit rate compared to similar learned models applied to equirectangular images. Also, compared to learning models based on graph convolutional networks, our solution supports more expressive filters that can preserve high frequencies and provide a better perceptual quality of the compressed images. Such results demonstrate the efficiency of the proposed framework, which opens new research venues for other omnidirectional vision tasks to be effectively implemented on the sphere manifold.

RESUMEN

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise.

Asunto(s)

Interpretación de Imagen Asistida por Computador , Multimedia , Aprendizaje Automático , Procesamiento de Señales Asistido por Computador , Grabación en Video

RESUMEN

Graph-based transforms are powerful tools for signal representation and energy compaction. However, their use for high dimensional signals such as light fields poses obvious problems of complexity. To overcome this difficulty, one can consider local graph transforms defined on supports of limited dimension, which may however not allow us to fully exploit long-term signal correlation. In this paper, we present methods to optimize local graph supports in a rate distortion sense for efficient light field compression. A large graph support can be well adapted for compression efficiency, however at the expense of high complexity. In this case, we use graph reduction techniques to make the graph transform feasible. We also consider spectral clustering to reduce the dimension of the graph supports while controlling both rate and complexity. We derive the distortion and rate models which are then used to guide the graph optimization. We describe a complete light field coding scheme based on the proposed graph optimization tools. Experimental results show rate-distortion performance gains compared to the use of fixed graph support. The method also provides competitive results when compared against HEVC-based and the JPEG Pleno light field coding schemes. We also assess the method against a homography-based low rank approximation and a Fourier disparity layer based coding method.

RESUMEN

Graph-based transforms have been shown to be powerful tools in terms of image energy compaction. However, when the size of the support increases to best capture signal dependencies, the computation of the basis functions becomes rapidly untractable. This problem is in particular compelling for high dimensional imaging data such as light fields. The use of local transforms with limited supports is a way to cope with this computational difficulty. Unfortunately, the locality of the support may not allow us to fully exploit long term signal dependencies present in both the spatial and angular dimensions of light fields. This paper describes sampling and prediction schemes with local graph-based transforms enabling to efficiently compact the signal energy and exploit dependencies beyond the local graph support. The proposed approach is investigated and is shown to be very efficient in the context of spatio-angular transforms for quasi-lossless compression of light fields.

RESUMEN

The paper addresses the problem of energy compaction of dense 4D light fields by designing geometry-aware local graph-based transforms. Local graphs are constructed on super-rays that can be seen as a grouping of spatially and geometry-dependent angularly correlated pixels. Both non separable and separable transforms are considered. Despite the local support of limited size defined by the super-rays, the Laplacian matrix of the non separable graph remains of high dimension and its diagonalization to compute the transform eigen vectors remains computationally expensive. To solve this problem, we then perform the local spatio-angular transform in a separable manner. We show that when the shape of corresponding super-pixels in the different views is not isometric, the basis functions of the spatial transforms are not coherent, resulting in decreased correlation between spatial transform coefficients. We hence propose a novel transform optimization method that aims at preserving angular correlation even when the shapes of the super-pixels are not isometric. Experimental results show the benefit of the approach in terms of energy compaction. A coding scheme is also described to assess the rate-distortion perfomances of the proposed transforms and is compared to state of the art encoders namely HEVC-lozenge [1], JPEG pleno 1.1 [2], HEVC-pseudo [3] and HLRA [4].

RESUMEN

We consider the synthesis of intermediate views of an object captured by two widely spaced and calibrated cameras. This problem is challenging because foreshortening effects and occlusions induce significant differences between the reference images when the cameras are far apart. That makes the association or disappearance/appearance of their pixels difficult to estimate. Our main contribution lies in disambiguating this ill-posed problem by making the interpolated views consistent with a plausible transformation of the object silhouette between the reference views. This plausible transformation is derived from an object-specific prior that consists of a nonlinear shape manifold learned from multiple previous observations of this object by the two reference cameras. The prior is used to estimate the evolution of the epipolar silhouette segments between the reference views. This information directly supports the definition of epipolar silhouette segments in the intermediate views, as well as the synthesis of textures in those segments. It permits to reconstruct the epipolar plane images (EPIs) and the continuum of views associated with the EPI volume, obtained by aggregating the EPIs. Experiments on synthetic and natural images show that our method preserves the object topology in intermediate views and deals effectively with the self-occluded regions and the severe foreshortening effect associated with wide-baseline camera configurations.

RESUMEN

Graph-based representation (GBR) has recently been proposed for describing color and geometry of multiview video content. The graph vertices represent the color information, while the edges represent the geometry information, i.e., the disparity, by connecting corresponding pixels in two camera views. In this paper, we generalize the GBR to multiview images with complex camera configurations. Compared with the existing GBR, the proposed representation can handle not only horizontal displacements of the cameras but also forward/backward translations, rotations, etc. However, contrary to the usual disparity that is a 2-D vector (denoting horizontal and vertical displacements), each edge in GBR is represented by a 1-D disparity. This quantity can be seen as the disparity along an epipolar segment. In order to have a sparse (i.e., easy to code) graph structure, we propose a rate-distortion model to select the most meaningful edges. Hence the graph is constructed with "just enough" information for rendering the given predicted view. The experiments show that the proposed GBR allows high reconstruction quality with lower or equivalent coding rate than traditional depth-based representations.

RESUMEN

Augmented reality, interactive navigation in 3D scenes, multiview video, and other emerging multimedia applications require large sets of images, hence larger data volumes and increased resources compared with traditional video services. The significant increase in the number of images in multiview systems leads to new challenging problems in data representation and data transmission to provide high quality of experience on resource-constrained environments. In order to reduce the size of the data, different multiview video compression strategies have been proposed recently. Most of them use the concept of reference or key views that are used to estimate other images when there is high correlation in the data set. In such coding schemes, the two following questions become fundamental: 1) how many reference views have to be chosen for keeping a good reconstruction quality under coding cost constraints? And 2) where to place these key views in the multiview data set? As these questions are largely overlooked in the literature, we study the reference view selection problem and propose an algorithm for the optimal selection of reference views in multiview coding systems. Based on a novel metric that measures the similarity between the views, we formulate an optimization problem for the positioning of the reference views, such that both the distortion of the view reconstruction and the coding rate cost are minimized. We solve this new problem with a shortest path algorithm that determines both the optimal number of reference views and their positions in the image set. We experimentally validate our solution in a practical multiview distributed coding system and in the standardized 3D-HEVC multiview coding scheme. We show that considering the 3D scene geometry in the reference view, positioning problem brings significant rate-distortion improvements and outperforms the traditional coding strategy that simply selects key frames based on the distance between cameras.

RESUMEN

In free viewpoint video systems, a user has the freedom to select a virtual view from which an image of the 3D scene is rendered, and the scene is commonly represented by color and depth images of multiple nearby viewpoints. In such representation, there exists data redundancy across multiple dimensions: 1) a 3D voxel may be represented by pixels in multiple viewpoint images (inter-view redundancy); 2) a pixel patch may recur in a distant spatial region of the same image due to self-similarity (inter-patch redundancy); and 3) pixels in a local spatial region tend to be similar (inter-pixel redundancy). It is important to exploit these redundancies during inter-view prediction toward effective multiview video compression. In this paper, we propose an encoder-driven inpainting strategy for inter-view predictive coding, where explicit instructions are transmitted minimally, and the decoder is left to independently recover remaining missing data via inpainting, resulting in lower coding overhead. In particular, after pixels in a reference view are projected to a target view via depth-image-based rendering at the decoder, the remaining holes in the target view are filled via an inpainting process in a block-by-block manner. First, blocks are ordered in terms of difficulty-to-inpaint by the decoder. Then, explicit instructions are only sent for the reconstruction of the most difficult blocks. In particular, the missing pixels are explicitly coded via a graph Fourier transform or a sparsification procedure using discrete cosine transform, leading to low coding cost. For blocks that are easy to inpaint, the decoder independently completes missing pixels via template-based inpainting. We apply our proposed scheme to frames in a prediction structure defined by JCT-3V where inter-view prediction is dominant, and experimentally we show that our scheme achieves up to 3-dB gain in peak-signal-to-noise-ratio in reconstructed image quality over a comparable 3D-High Efficiency Video Coding implementation using fixed 16 $\times $ 16 block size.

RESUMEN

In this paper, we propose a new geometry representation method for multiview image sets. Our approach relies on graphs to describe the multiview geometry information in a compact and controllable way. The links of the graph connect pixels in different images and describe the proximity between pixels in 3D space. These connections are dependent on the geometry of the scene and provide the right amount of information that is necessary for coding and reconstructing multiple views. Our multiview image representation is very compact and adapts the transmitted geometry information as a function of the complexity of the prediction performed at the decoder side. To achieve this, our graph-based representation (GBR) carefully selects the amount of geometry information needed before coding. This is in contrast with depth coding, which directly compresses with losses the original geometry signal, thus making it difficult to quantify the impact of coding errors on geometry-based interpolation. We present the principles of this GBR and we build an efficient coding algorithm to represent it. We compare our GBR approach to classical depth compression methods and compare their respective view synthesis qualities as a function of the compactness of the geometry description. We show that GBR can achieve significant gains in geometry coding rate over depth-based schemes operating at similar quality. Experimental results demonstrate the potential of this new representation.

RESUMEN

Enabling users to interactively navigate through different viewpoints of a static scene is a new interesting functionality in 3D streaming systems. While it opens exciting perspectives toward rich multimedia applications, it requires the design of novel representations and coding techniques to solve the new challenges imposed by the interactive navigation. In particular, the encoder must prepare a priori a compressed media stream that is flexible enough to enable the free selection of multiview navigation paths by different streaming media clients. Interactivity clearly brings new design constraints: the encoder is unaware of the exact decoding process, while the decoder has to reconstruct information from incomplete subsets of data since the server generally cannot transmit images for all possible viewpoints due to resource constrains. In this paper, we propose a novel multiview data representation that permits us to satisfy bandwidth and storage constraints in an interactive multiview streaming system. In particular, we partition the multiview navigation domain into segments, each of which is described by a reference image (color and depth data) and some auxiliary information. The auxiliary information enables the client to recreate any viewpoint in the navigation segment via view synthesis. The decoder is then able to navigate freely in the segment without further data request to the server; it requests additional data only when it moves to a different segment. We discuss the benefits of this novel representation in interactive navigation systems and further propose a method to optimize the partitioning of the navigation domain into independent segments, under bandwidth and storage constraints. Experimental results confirm the potential of the proposed representation; namely, our system leads to similar compression performance as classical inter-view coding, while it provides the high level of flexibility that is required for interactive streaming. Because of these unique properties, our new framework represents a promising solution for 3D data representation in novel interactive multimedia services.