Compression of multiview images using a sparse layer-based representation

Multiview images are obtained by recording a scene from different viewpoints. The additional information can be used to improve the performance of various applications ranging from e-commerce to security surveillance. Many such applications process large arrays of images, and therefore it is important to consider how the information is stored and transmitted. In this thesis we address the issue of multiview image compression. Our approach is based on the concept that a point in a 3D space maps to a constant intensity line in specific multiview image arrays. We use this property to develop a sparse representation of multiview images. To obtain the representation we segment the data into layers, where each layer is defined by an object located at a constant depth in the scene. We extract the layers by initialising the layer contours and then by iteratively evolving them in the direction which minimises an appropriate cost function. To obtain the sparse representation we reduce the redundancy of each layer by using a multi-dimensional discrete wavelet transform (DWT). We apply the DWT in a separable approach; first across the camera viewpoint dimensions, followed by a 2D DWT applied to the spatial dimensions. The camera viewpoint DWT is modified to take into account the structure of each layer, and also the occluded regions. Based on the sparse representation, we propose two compression algorithms. The first is a centralised approach, which achieves a high compression, however requires the transmission of all the data. The second is an interactive method, which trades-off compression performance in order to facilitate random access to the multiview image dataset. In addition, we address the issue of rate allocation between encoding of the layer contours and the texture. We demonstrate that the proposed centralised and interactive methods outperform H.264/MVC and JPEG 2000, respectively

Compression of multiview images using a sparse layer-based representation

Multiview images are obtained by recording a scene from different viewpoints. The additional information can be used to improve the performance of various applications ranging from e-commerce to security surveillance. Many such applications process large arrays of images, and therefore it is important to consider how the information is stored and transmitted. In this thesis we address the issue of multiview image compression. Our approach is based on the concept that a point in a 3D space maps to a constant intensity line in specific multiview image arrays. We use this property to develop a sparse representation of multiview images. To obtain the representation we segment the data into layers, where each layer is defined by an object located at a constant depth in the scene. We extract the layers by initialising the layer contours and then by iteratively evolving them in the direction which minimises an appropriate cost function. To obtain the sparse representation we reduce the redundancy of each layer by using a multi-dimensional discrete wavelet transform (DWT). We apply the DWT in a separable approach; first across the camera viewpoint dimensions, followed by a 2D DWT applied to the spatial dimensions. The camera viewpoint DWT is modified to take into account the structure of each layer, and also the occluded regions. Based on the sparse representation, we propose two compression algorithms. The first is a centralised approach, which achieves a high compression, however requires the transmission of all the data. The second is an interactive method, which trades-off compression performance in order to facilitate random access to the multiview image dataset. In addition, we address the issue of rate allocation between encoding of the layer contours and the texture. We demonstrate that the proposed centralised and interactive methods outperform H.264/MVC and JPEG 2000, respectively.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multiview image compression using a layer-based representation

The authors propose a novel compression method for multiview images. The algorithm exploits the layer-based representation, which partitions the data set into planar layers characterized by a constant depth value. For efficient compression, the partitioned data is decorrelated using the separable three-dimensional wavelet transform across the viewpoint and spatial dimensions. The transform is modified to efficiently deal with occlusions and disparity variations for different depths. The generated transform coefficients are entropy coded. Experimental results show that our coding method is capable of outperforming the state-of-the-art algorithms, like H.264/AVC, for different data sets

Layer based multi-view image compression

The authors propose a compression algorithm for an array of multiview images. First, we apply a segmentation algorithm to partition the data into coherent layers and significantly reduce the number of images required for artifact-free rendering. Then, we exploit the coherence in each layer by applying a 1D disparity compensated wavelet transform across the views followed by a 2D SA-DWT on each of the spatial subbands. Finally, the data is entropy coded using a modified version of EBCOT. Experimental results show that our coder outperforms state-of-the-art H.264/AVC at low bit-rates and intra-image JPEG-2000 over the complete range of bit-rates. Furthermore, unlike other multi-view image compression techniques, our implementation does not rely on estimating a 3D geometric model of the scene

Multiview image coding using depth layers and an optimized bit allocation

The authors present a novel wavelet-based compression algorithm for multiview images. This method uses a layer-based representation, where the 3-D scene is approximated by a set of depth planes with their associated constant disparities. The layers are extracted from a collection of images captured at multiple viewpoints and transformed using the 3-D discrete wavelet transform (DWT). The DWT consists of the 1-D disparity compensated DWT across the viewpoints and the 2-D shape-adaptive DWT across the spatial dimensions. Finally, the wavelet coefficients are quantized and entropy coded along with the layer contours. To improve the rate-distortion performance of the entire coding method, we develop a bit allocation strategy for the distribution of the available bit budget between encoding the layer contours and the wavelet coefficients. The achieved performance of our proposed scheme outperforms the state-of-the-art codecs for several data sets of varying complexity

Interactive multiview image coding

The authors propose a novel multiview compression method for multiview images. The algorithm supports random access for interactive applications and has low storage requirements. The fundamental component of the method is the layer-based representation, which partitions the dataset into redundant layers characterized by a constant depth value. We exploit the redundant property of each layer and remove the side information uncertainty using Distributed Source Coding (DSC) principles. In comparison to independent coding, our method achieves a PSNR improvement of 3dB. Furthermore, we present a rate-distortion (RD) analysis which demonstrates that the proposed algorithm can achieve a better performance in comparison to independent coding