Progressive contour coding in the wavelet domain

This paper presents a new wavelet-based image contour coding technique, suitable for representing either shapes or generic contour maps. Starting from a contour map (e.g. a segmentation map or the result of a contour extraction operator), this is first converted in a one-dimensional signal. Coordinate jumps among different contour extremities are converted, if under a suitable threshold, into signal discontinuities which can be compactly represented in the wavelet domain. Otherwise, the exceeding discontinuities are coded as side information. This side information is minimized by an optimized contour segment sequencing. The obtained 1D signal is decomposed and coded in the wavelet domain by using a 1D version of an improved implementation of the SPIHT algorithm. This technique can efficiently code every kind of 2D contour map, from one to many unconnected contour segments. It guarantees a fully embedded progressive coding, state-of-art coding performance, good approximation capabilities for both open and closed contours, and visually graceful degradation at low bit-rates

NEW CHANGE DETECTION MODELS FOR OBJECT-BASED ENCODING OF PATIENT MONITORING VIDEO

The goal of this thesis is to find a highly efficient algorithm to compress patient monitoring video. This type of video mainly contains local motions and a large percentage of idle periods. To specifically utilize these features, we present an object-based approach, which decomposes input video into three objects representing background, slow-motion foreground and fast-motion foreground. Encoding these three video objects with different temporal scalabilities significantly improves the coding efficiency in terms of bitrate vs. visual quality. The video decomposition is built upon change detection which identifies content changes between video frames. To improve the robustness of capturing small changes, we contribute two new change detection models. The model built upon Markov random theory discriminates foreground containing the patient being monitored. The other model, called covariance test method, identifies constantly changing content by exploiting temporal correlation in multiple video frames. Both models show great effectiveness in constructing the defined video objects. We present detailed algorithms of video object construction, as well as experimental results on the object-based coding of patient monitoring video