Many convolutional neural networks (CNNs) for single image deblurring employ a U-Net structure to estimate latent sharp images. Having long been proven to be effective in image restoration tasks, a single lane of encoder-decoder architecture overlooks the characteristic of deblurring, where a blurry image is generated from complicated blur kernels caused by tangled motions. Toward an effective network architecture for single image deblurring, we present complemental sub-solution learning with a one-encoder-two-decoder architecture. Observing that multiple decoders successfully learn to decompose encoded feature information into directional components, we further improve both the network efficiency and the deblurring performance by rotating and sharing kernels exploited in the decoders, which prevents the decoders from separating unnecessary components such as color shift. As a result, our proposed network shows superior results compared to U-Net while preserving the network parameters, and using the proposed network as the base network can improve the performance of existing state-of-the-art deblurring networks.

In scalable video coding (SVC) as an extension of H.264/advanced video coding (AVC), a computationally expensive exhaustive mode decision is employed to select the best coding mode for each macroblock (MB). In order to reduce computational complexity, we propose a fast mode decision algorithm for SVC which uses an all-zero block (AZB) detection. Based on the empirical analysis of the inter-layer correlation of the AZB, the MB at the enhancement layer (EL) is predicted whether to be the AZB. Then, only predicted MBs are examined by the AZB detection algorithm. Since the mode decision can be terminated by detecting the AZB, we determine the processing order of the candidate modes at the EL in order to terminate the mode decision in the early stage. The proposed algorithm can be combined with other conventional fast mode decision methods in order to further reduce the computational complexity of those methods. Experimental results show that the proposed algorithm can significantly speed up the encoding process, especially for low bit-rate video sequences, without deteriorating the coding efficiency of SVC.

The scalable video coding (SVC) standard adopts a simple interlayer intra prediction (ILIP) method for encoding scalable video sequences. In the conventional ILIP, a prediction signal for the macroblock (MB) of the enhancement layer (EL) is obtained by simply upsampling the colocated block of the base layer (BL). We propose an improved ILIP method by generalizing the original one adopted in the SVC standard. In the proposed ILIP method, the MB of the EL is predicted using all MBs of the BL. Experimental results show that the proposed algorithm can reduce the bit rate by 1.91% to 6.44%, as compared with the conventional ILIP, while the average PSNR is not decreased.

This paper presents a policy-based hybrid management architecture for IP-based VPN (virtual private network) service that makes use of a centralized global policy management server.