High-quality magnetic resonance (MR) images afford more detailed information for reliable diagnoses and quantitative image analyses. Given low-resolution (LR) images, the deep convolutional neural network (CNN) has shown its promising ability for image…
Tile-based rate adaption can improve the quality of experience (QoE) for adaptive 360-degree video streaming under constrained network conditions, which, however, is a challenging problem due to the requirements of accurate prediction for users’…
Domain adaptation aims to exploit domain-invariant features by aligning the cross-domain distributions in the manifold subspace for applying the classifier trained on the source domain to the target domain. However, two limitations may still deteriorat…
Dynamically adaptive streaming over HTTP requires a large-scale server to transcode various bitrate versions in which different preset parameters can be used to provide different video qualities at each resolution. When transcoding servers contain a he…
To address the problem of inadequate feature extraction and binary code discrete optimization faced by deep hashing methods using a relaxation-quantization strategy, a novel deep attention-guided hashing method with pairwise labels (DAHP) is proposed t…
The development of technologies that can generate Deepfake videos is expanding rapidly. These videos are easily synthesized without leaving obvious traces of manipulation. Though forensically detection in high-definition video datasets has achieved rem…
Human visual system has a limitation of sensitivity in detecting small distortion in an image/video and the minimum perceptual threshold is so called Just Noticeable Difference (JND). JND modelling is challenging since it highly depends on visual conte…
Automatically synthesizing sounds for different visual contents poses a challenge and there is a strong need to facilitate the direct creation of realistic sounds. Different from previous works, in this paper, we propose a novel deep learning based app…
Block permutation and Co-XOR (BPCX) image encryption is a commonly used encryption method for reversible data hiding in the encryption domain, which can effectively improve the embedded capacity and the ability resisting the existing attacks including …
Although massive pruning methods are emerging for achieving structural sparsity in convolutional neural networks (CNN), most of them target structures such as ResNet. Meanwhile, previous works take more interest in pruning filters inside a residual blo…