Practical applications for multi-view video such as three-dimensional television may require to transmit the data over error-prone channels. Even when channel coding is used, it is likely that parts of the image information are lost after decoding. To improve the image quality in this case, an efficient algorithm for multi-view error concealment is presented. Extending and enhancing a previously known method for single-view concealment, the algorithm simultaneously uses information from surrounding image parts, from temporally preceding and succeeding frames and from neighbouring camera views for extrapolating known image samples into the lost area. It is shown that the proposed algorithm leads to convincing results in terms of PSNR as well as to a good subjective quality. Compared to single-view concealment, the result is improved by up to 0.75 dB by adding cross-view information.

Significant advances have recently been made in the coding of video data recorded with multiple cameras. However, luminance and chrominance variations between the camera views may deteriorate the performance of multiview codecs and image-based rendering algorithms. A histogram matching algorithm can be applied to efficiently compensate for these differences in a prefiltering step. A mapping function is derived which adapts the cumulative histogram of a distorted sequence to the cumulative histogram of a reference sequence. If all camera views of a multiview sequence are adapted to a common reference using histogram matching, the spatial prediction across camera views is improved. The basic algorithm is extended in three ways: a time-constant calculation of the mapping function, RGB color conversion, and the use of global disparity compensation. The best coding results are achieved when time-constant histogram calculation and RGB color conversion are combined. In this case, the usage of histogram matching prior to multiview encoding leads to substantial gains in the coding efficiency of up to 0.7 dB for the luminance component and up to 1.9 dB for the chrominance components. This prefiltering step can be combined with block-based illumination compensation techniques that modify the coder and decoder themselves, especially with the approach implemented in the multiview reference software of the joint video team (JVT). Additional coding gains up to 0.4 dB can be observed when both methods are combined.

A wavelet-based approach to scalable multi-view video coding (MVC) is examined in this paper. A 4-D wavelet transform is used to decorrelate the multi-view video data temporally, view-directionally, and spatially for efficient compression. Motion compensated temporal filtering (MCTF) is applied to each video sequence of each camera to exploit temporal correlation and inter-view dependencies are exploited with disparity compensated view filtering (DCVF). Together with a 2-D spatial wavelet transform the 4-D wavelet transform is constituted. The open-loop structure of the decomposition, together with embedded quantization and coding, allows for the construction of a bitstream which is fully scalable in temporal, view-directional, spatial and quality dimension. Results for scalable decoding in all dimensions are given, as well as comparisons to simulcast coding and state-of-the-art non-scalable multi-view video coding. Further, a significant coding gain is shown when the sequences are pre-processed with an algorithm for cross-view illumination compensation based on histogram matching.

In this paper, a novel framework for scalable multi-view video coding is described. A well known wavelet based scalable coding scheme for single-view video sequences has been adopted and extended to match the specific needs of scalable multi-view video coding. Motion compensated temporal filtering (MCTF) is applied to each video sequence of each camera. The use of a wavelet lifting structure guarantees perfect invertibility of this step, and as a consequence of its open-loop architecture, SNR and temporal scalability are attained. Correlations between the temporal subbands of adjacent cameras are reduced by a novel disparity compensated view filtering (DCVF), method which is also lifting based and open-loop to enable view scalability. Spatial scalability and entropy coding are achieved by the JPEG2000 spatial wavelet transform and EBCOT coding, respectively. Rate allocation along the temporal-view-filtered subbands is done by means of an RD-optimal algorithm. Experimental results show the high scaling capability in terms of SNR, temporal and view scalability