Image-dependent shape coding and representation

Abstract

We present a new shape-coding algorithm to support object-based representation, which differs from previous algorithms in that it encodes shape as dependent meta data for image description. Therefore, both the shape-coding and decoding processes of this algorithm are designed to be dependent on the underlying image in which the object (described by the shape) is contained. This way, the correlation between image and shape is effectively removed and the shape-coding efficiency is improved on average by three times over the state-of-the-art algorithms. To facilitate comparison, a generalized "contour-generating" framework is introduced to formulate the shape-coding problem. From this framework we derive both the proposed algorithm and a number of state-of-the-art algorithms, and show that the rate-distortion (RD) criterion can be studied in a uniform way under this framework. Specifically, a dynamic-programming-based algorithm is designed to find the RD optimal coding result for the proposed algorithm. As an extension, we also discuss the complexity and scalability issues related to the application design of the proposed algorithm.