Distributed video coding （DVC） arouses high interests due to its property of low-complexity encoding. This paper proposes a robust multiple description DVC （MDDVC） under the constraint of low-complexity encoding. In MDDVC, zeros are padded to each frame and the resulting big-size video is divided into multiple descriptions. Then, each description is compressed by s hybrid DVC （HDVC） codec and trans- mitted over different channel. When one channel does not work, the lost HDVC description is estimated by the received from other channel, which guarantees the robustness of the system; MDDVC moves the complex motion estimation totally to the decoder so it features low-complexity encoding. In the pre-processing, an optimized zero-padding is also proposed to improve the performance. Experimental results exhibit that the proposed MDDVC scheme achieves better rate-distortion performance and robustness than the referenced especially when packet-loss rate is high.
A novel Iossless data hiding scheme based on a combination of prediction and the prediction-error adjustment （PEA） is presented in this paper. For one pixel, its four surrounding neighboring pixels are used to predict it and 1-bit watermark information is embedded into the prediction-error. In traditional approaches, for the purpose of controlling embedding distortion, only pixels with small predictionerrors are used for embedding. However, when the threshold is small, it is difficult to efficiently compress the location map which is used to identify embedding locations. Thus, PEA is introduced to make large prediction-error available for embedding while causing low embedding distortions, and accordingly, the location map can be compressed well. As a result, the hiding capacity is largely increased. A series of experiments are conducted to verify the effectiveness and advantages of the proposed approach.
