Reconstruction-optimized lapped orthogonal transforms for robust image transmission

Wireless transmission of compressed visual information presents new challenges in image coding and reconstruction techniques. Wireless channels do not offer guaranteed transmission, and data loss over such channels can result in catastrophic errors in the decoded visual information. Visual data, however, can be reconstructed using lossy signal processing techniques. To date, reconstruction algorithms have been developed for fixed coding techniques. This paper presents and solves the dual problem-a block-based coding technique, namely a family of lapped orthogonal transforms (LOTs) is designed to maximize the reconstruction performance of a specified reconstruction algorithm. Mean-reconstruction, in which a missing coefficient block is replaced with the average of its available neighbors, is selected for its simplicity and ease of implementation. A reconstruction criterion is defined as the equal distribution of reconstruction errors across all transform coefficients, and a family of LOTs is then designed to meet the reconstruction criterion as well as consider the transform coding gain. Reconstruction capability and coding gain are traded off, and the LOT family consists of transforms that provide increasing reconstruction capability with lower coding gain. The reconstruction-optimized LOT family provides excellent reconstruction capability, and a transform can be selected based on the loss characteristics of the channel, the desired reconstruction performance, and the desired compression