Locally adaptive reconstruction of lost low frequency coefficients in wavelet coded images In packet switched networks such as the Internet, packets may get lost during transmission due to, e.g., network congestion. This leads to a quality degradation of the original signal. As video communication is a bandwidth consuming application, the original data are first compressed. This compression step increases the impact of information loss even more. In wavelet based image and video coding, the low frequency data is the most important. Loss of low frequency coefficients results in annoying black holes in the received images and video. This effect can be countered by post processing error concealment: a lost coefficient is estimated from its neighboring coefficients. In this paper we present a locally adaptive interpolation method for the lost low frequency coefficients. For each lost low frequency coefficient, we estimate the optimal interpolation direction (horizontal or vertical) using novel error measures. In this way, we preserve the edges in the reconstructed image much better. Compared to older techniques of similar complexity, our scheme reconstructs images with the same or better quality. This is reflected in the visual as well as in the numerical results: there is an increase of up to 4.4 dB compared to bilinear concealment. The proposed scheme is fast and simple, which makes it suitable for realtime applications.

Novel sequential error concealment techniques using orientation adaptive interpolation This paper introduces a new framework of sequential error concealment techniques for blockbased image coding systems. Unlike previous approaches which simultaneously recover the pixels inside the missing block, we propose to recover them in a sequential fashion. The structure of sequential recovery enhances the capability of handling complex texture patterns in the image and serious block loss situations during the transmission. Under the framework of sequential recovery, we present a novel spatially adaptive scheme to interpolate the missing pixels along the edge orientation. We also study the problem of how to fully exploit the information from the available surrounding neighbors with the sequential constraint. Experiment results have shown that novel sequential recovery techniques are superior to most existing parallel recovery techniques in terms of both subjective and objective quality of reconstructed images.