Sparse representation of depth maps for efficient transform coding

Compression of depth maps is important for “image plus depth” representation of multiview images, which enables synthesis of novel intermediate views via depth-image-based rendering (DIBR) at decoder. Previous depth map coding schemes exploit unique depth characteristics to compactly and faithfully reproduce the original signal. In contrast, given that depth maps are not directly viewed but are only used for view synthesis, in this paper we manipulate depth values themselves, without causing severe synthesized view distortion, in order to maximize sparsity in the transform domain for compression gain. We formulate the sparsity maximization problem as an l₀-norm optimization. Given l₀-norm optimization is hard in general, we first find a sparse representation by iteratively solving a weighted l₁ minimization via linear programming (LP). We then design a heuristic to push resulting LP solution away from constraint boundaries to avoid quantization errors. Using JPEG as an example transform codec, we show that our approach gained up to 2.5 dB in rate-distortion performance for the interpolated view.