Showing papers by "Junhui Hou published in 2012"

Dynamic 3-D facial compression using low rank and sparse decomposition

Abstract: In this paper, we propose a new compression framework for dynamic 3-D facial expressions acquired from structured light based 3-D camera, based on our previous work. Taking advantage of the near-isometric property of human facial expressions, we parameterize the dynamic 3-D faces into an expression-invariant canonical domain, which naturally generates geometry video and allows us to apply the well-studied video compression technique. Then, low rank and sparse decomposition is applied to each dimension (i.e., X, Y and Z, respectively) before the H.264/AVC encoder is employed to separately encode each dimension instead of encoding them as a whole. Experimental results show that the averaged 3-4 dB gain is achieved by the proposed scheme compared with existing algorithms.

A new rate-quantization model for H.264/AVC low-delay rate control

Abstract: In this paper, we present a new rate-quantization (R-Q) model for H.264/AVC low-delay rate control. Our rate model is a power function of the quantization stepsize, which is derived through theoretical analysis assuming the Laplacian distributed source. Model parameters are content adaptive and updated frame by frame. Proposed R-Q model is implemented on H.264/AVC reference software JM17.2 to evaluate the performance, where accurate quantization stepsize is selected for each frame according to the target bit rate. As compared to the most recent published work [5] and well-known R-Q model adopted in JM [13], we have shown that almost 2x performance improvement in terms of the bits mismatch error.