Abstract-We propose a wavelet-based codec for the static Depth-Image-Based Representation (DIBR), which allows viewers to freely choose the viewpoint. The proposed codec jointly estimates and encodes the unknown depth map from multiple views using a novel Rate-Distortion (RD) optimization scheme. The rate constraint reduces the ambiguity of depth estimation by favoring piecewise-smooth depth maps. The optimization is efficiently solved by a novel dynamic programming along trees of integer wavelet coefficients. The codec encodes the image and the depth map jointly to decrease their redundancy and to provide a RD-optimized bitrate allocation between the two. The codec also offers scalability both in resolution and in quality. Experiments on real data show the effectiveness of the proposed codec.

We present a novel codec of depth-image-based representations for free-viewpoint 3D-TV. The proposed codec relies on a shape-adaptive wavelet transform and an explicit representation of the locations of major depth edges. Unlike classical wavelet transforms, the shape-adaptive transform generates small wavelet coefficients along depth edges, which greatly reduces the data entropy. The codec also shares the edge information between the depth map and the image to reduce their correlation. The wavelet transform is implemented by shape-adaptive lifting, which enables fast computations and perfect reconstruction. Experimental results on real data confirm the superiority of the proposed codec, with PSNR gains of up to 5.46dB on the depth map and up to 0.19dB on the image compared to standard wavelet codecs.

We present a novel depth and depth-color codec aimed at free-viewpoint 3D-TV. The proposed codec uses a shape-adaptive wavelet transform and an explicit encoding of the locations of major depth edges.Unlike the standard wavelet transform, the shape-adaptive transform generates small wavelet coefficients along depth edges, which greatly reduces the bits required to represent the data. The wavelet transform is implemented by shape-adaptive lifting, which enables fast computations and perfect reconstruction.We derive a simple extension of typical boundary extrapolation methods for lifting schemes to obtain as many vanishing moments near boundaries as away from them. We also develop a novel rate-constrained edge detection algorithm, which integrates the idea of significance bitplanes into the Canny edge detector.Together with a simple chain code, it provides an efficient way to extract and encode edges. Experimental results on synthetic and real data confirm the effectiveness of the proposed codec, with PSNR gains of more than 5 dB for depth images and significantly better visual quality for synthesized novel view images.

No abstract

No abstract