In this paper, we address the stereo matching problem in stereo videos using partially informed Markov Random Fields (MRFs) using the motion information between subsequent frames as a side information. We use the motion vectors within one of the videos to regularize the disparity estimate using this motion eld. The proposed scheme enables us to obtain good disparity estimates using faster and simpler disparity nding algorithms in each step.
In the framework of Wyner-Ziv Coding of Video, the coding efficiency depends on the quality of the side
information (SI) at the decoder, where the side information is constructed from the key frames available at the
decoder. In this paper, we propose a novel frame interpolation method for Wyner-Ziv coding, where the motion
compensation is bidirectional and allows pixelwise estimation. The proposed interpolation method allows to
obtain better SI quality than the one obtained by state-of-the-art interpolation methods.
Channel Coding with Side Information at the encoder(CCSI) can be visualized as a blind watermarking problem: the original host signal for embedding the watermark is known at the encoder but not at the decoder. Similarly, the Rate Distortion with Side Information at the decoder(RDSI) is known as distributed source coding: the rate distortion limits of an input source if a noisy observation of that source is available only at the decoder. There is a strong duality between CCSI and RDSI for the gaussian case. We propose a system that exploits the generalized versions of the two information theoretical dualities of CCSI and RDSI together within a unique setup. The question is "Can we combine these two separated dual problems (blind watermarking and distributed source coding) within a single problem?". The proposed scheme can be viewed as "Watermarking or Data Hiding within Distributed Source Coding". The setup contains the cascade of the generalized versions of CCSI and RDSI where there exists two different side information, one available only at the encoder and the other at the decoder. The preliminary experimental results are given using the theoretical findings of the duality problem.
Attentive robots have visual systems with fovea-periphery distinction and saccadic motion capability. Previous work has shown that spatial and temporal redundancy thus present can be exploited in video coding/streaming algorithms and hence considerable bandwidth efficiency can be achieved. In this paper, we present a complete framework for real-time video coding with integrated pre-attentive processing and show that areas of greatest interest can be ensured of being processed in greater detail. The first step is pre-attention where the goal is to fixate on the most interesting parts of the incoming scene using a measure of saliency. The construction of the pre-attention function can vary depending on the set of visual primitives used. Here, we use Cartesian and Non-Cartesian filters and build a pre-attention function for a specific problem -- namely video coding in applications such as robot-human tracking or
video-conferencing. Using the most salient and distinguishing filter responses as the input, system parameters of a neural network are trained using resilient back-propagation algorithm with supervised learning. These parameters are then used in the construction of the pre-attentive function. Comparative results indicate that even with a very limited amount of learning, performance robustness can be achieved.