Description:
In a transform domain distributed video coding scheme, the correlation between the current encoding unit, e.g. block and slice, and the corresponding side-information is modeled using a virtual channel. This correlation model is then used for rate allocation, quantization, and Wyner-Ziv coding. Since the encoder can only have an estimate of the correlation instead of the exact knowledge of the side-information, the decoder will fail to recover the quantized transformed coeffi- cients with a nonzero probability. In this paper, we propose to integrate a scheme at the decoder to recover the undecoded coefficients using the spatial smoothness property of individual video frames. Simulation results demonstrated that, at different decoding failure probabilities, a transformed coeffi- cient recovery scheme can significantly improve the quality of videos in terms of both PSNR and SSIM.
Description:
In a transform domain distributed video coding scheme, the correlation between the current encoding unit, e.g. block and slice, and the corresponding side-information is modeled using a virtual channel. This correlation model is then used for rate allocation, quantization, and Wyner-Ziv coding. Since the encoder can only have an estimate of the correlation instead of the exact knowledge of the side-information, the decoder will fail to recover the quantized transformed coeffi- cients with a nonzero probability. In this paper, we propose to integrate a scheme at the decoder to recover the undecoded coefficients using the spatial smoothness property of individual video frames. Simulation results demonstrated that, at different decoding failure probabilities, a transformed coeffi- cient recovery scheme can significantly improve the quality of videos in terms of both PSNR and SSIM
Description:
The task of classifying the genre of polyphonic music signals is traditionally done using only low level features of the signal. In this paper high level features have been applied to improve the task of music genre classification. The use of statistical chord features and chord progression information in conjunction with low level features are proposed in this paper. The chord progression information is manifested in genre probability descriptors calculated using a pattern matching algorithm. Our proposed method provides an improvement of 12.4% in the classification results over a commonly compared technique.