In this paper, we propose an efficient scheme to transport video over wireless networks, specifically cdma2000® 1x. Speech transmission over cdma2000® uses a variable rate voice coder (vocoder) over a channel with multiple fixed rates. We apply these ideas to compressed video transmission over wireless IP networks. Explicit Bit Rate (EBR) video compression is designed to match the video encoder output to a set of fixed channel rates. We show that in comparison with VBR video transmission over a fixed rate wireless channel, EBR video transmission provides improved error resilience, reduced latency and improved efficiency.

In this work we tackle the problem of error propagation that packet losses can cause in commonly used predictive video coding environments. Using unbalanced multiple description coding (UMDC) to generate redundant source data, we apply the consistency sequence estimation (CSE) algorithm for estimating the lost data. The CSE algorithm, proposed by Singh and Ortega for a 1-D input source signal in a DPCM context, uses a sequence search to verify the consistency of the estimates with the received data. The novelty of our work is the extension of the CSE algorithm to a block matching ME/MC video coder (such as H.263). To reach this aim we propose to work in the spatial domain. Among the advantages of the proposed error resilience scheme are its low-complexity and its compatibility with standard video coders.

We introduce a novel rate-distortion (RD) model for images coded with a progressive wavelet coder, such as SPIHT (set partitioning in hierarchical trees) or JPEG2000, and especially designed to capture RD behavior when different parts of an image are refined at different speeds. Our model is an extension of Mallat's model (Mallat and Falzon 1998), which takes into account that the rates used are not necessarily the same throughout the image. Because of the different rates, certain modeling approximations (e.g., those for coarse quantization) can not be used uniformly throughout the image. Allocating different numbers of bits to different regions in an image is a problem that appears in applications such as region of interest (ROI) coding or multiple description coding (MDC), where the wavelet coefficients are divided by different factors before coding to enable different bit allocations to different regions. We show that the proposed RD model provides a more accurate estimate than Mallat's model.

We propose novel watermark embedding and detection methods to address the problem of copyright tracking of movies distributed to theaters by satellites. Temporal and spatial wavelet transformation as well as feature-based watermark embedding procedures are deployed such that the proposed watermarking algorithm is able to affect a compromise tradeoff between the visual quality and the robustness. The multi-resolution nature of the wavelet transformations of a video sequence makes the watermark very robust and secure. Wavelet features make huge watermark patterns possible, which allows unique labeling of huge number of videos and video sequences dynamically. Changing some of the features in the video wavelet transformation domain according to the defined rules makes the watermark detection semi-oblivious, which allows watermark detection very robust and independent without the original sequence. Our proposed watermark can also be detected robustly after many kinds of malicious pirating attacks and modifying, and experimental results are reported in this paper.

We address the problem of allocating bits to the different regions in an image coded with a progressive wavelet coder such as SPIHT (Set Partitioning in Hierarchical Trees) [1]. This type of problem appears in applications such as Region of Interest (ROI) coding or Multiple Description Coding (MDC). The wavelet coefficients in both cases are divided by different factors before coding to enable different bit allocation to different regions, because the coefficients in each region are refined at different speeds. While this is a popular approach for ROI coding [2, 3], we propose using it for MDC as well. In this work, we introduce a priority scaling factor ( ) as a dividing factor. The main contribution of this work is to provide an analytical technique to determine what the should be, given criteria such as relative importance of the regions in ROI coding or degree of redundancy in an MDC. Our approach is based on an approximation to Mallat's model [4]. We show how our selection of is basically the same as that obtained by optimization of empirical data, with significantly less complexity. 1.