Statistical L-infinite distortion estimation in scalable coding of meshes

Abstract

This paper investigates the novel concept of local error control in arbitrary mesh encoding, and proposes a new L-infinite mesh coding approach implementing this concept. In contrast to traditional mesh coding systems that use the mean-square error as distortion measure, the proposed approach employs the L-infinite distortion as target distortion metric. In this context, a novel wavelet-based L-infinite-constrained coding approach for meshes is proposed, which ensures that the maximum local error between the original and decoded meshes is lower than a given upper-bound. Additionally, the proposed system achieves scalability in L-infinite sense, that is, the L-infinite distortion upper-bound can be accurately estimated when decoding any layer from the input stream. Moreover, a distortion estimation approach is proposed, expressing the L-infinite distortion in the spatial domain as a statistical estimate of quantization errors produced in the wavelet domain. An instantiation of the proposed L-infinite coding approach is demonstrated for MESHGRID, which is a scalable 3D object coding system, part of MPEG-4 AFX. The proposed L-infinite coding approach guarantees that the maximum error is upper-bounded, it enables a fast real-time implementation of the rate-allocation, and it preserves all the scalability features and animation capabilities of the employed scalable mesh codec.