We present a novel Convolutional Neural Network that exploits the Laplacian decomposition technique, which is typically used in traditional image processing, to restore videos compressed with the High-Efficiency Video Coding (HEVC) algorithm. The proposed method decomposes the compressed frames into multi-scale frequency bands using the Laplacian decomposition, it restores each band using the ad-hoc designed Multi-frame Residual Laplacian Network (MRLN), and finally recomposes the restored bands to obtain the restored frames. By leveraging the multi-scale frequency representation of compressed frames provided by the Laplacian decomposition, MRLN can effectively reduce the compression artifacts and restore the image details with a reduced computational cost. In addition, our method can be easily instantiated in various versions to control the tradeoff between efficiency and effectiveness, representing a versatile solution for scenarios with constrained computational resources. Experimental results on the MFQEv2 benchmark dataset show that our method achieves the state-of-the-art performance in HEVC-compressed video restoration with a lower model complexity and shorter runtime with respect to existing methods. The project page is available at https://github.com/claudiom4sir/LaplacianVCAR .

Low-light video enhancement (LLVE) has received little attention compared to low-light image enhancement (LLIE) mainly due to the lack of paired low-/normal-light video datasets. Consequently, a common approach to LLVE is to enhance each video frame individually using LLIE methods. However, this practice introduces temporal inconsistencies in the resulting video. In this work, we propose a recurrent neural network (RNN) that, given a low-light video and its per-frame enhanced version, produces a temporally consistent video preserving the underlying frame-based enhancement. We achieve this by training our network with a combination of a new forward-backward temporal consistency loss and a content-preserving loss. At inference time, we can use our trained network to correct videos processed by any LLIE method. Experimental results show that our method achieves the best trade-off between temporal consistency improvement and fidelity with the per-frame enhanced video, exhibiting a lower memory complexity and comparable time complexity with respect to other state-of-the-art methods for temporal consistency.

Android apps must work correctly even if their execution is interrupted by external events. For instance, an app must work properly even if a phone call is received, or after its layout is redrawn because the smartphone has been rotated. Since these events may require destroying, when the execution is interrupted, and recreating, when the execution is resumed, the foreground activity of the app, the only way to prevent the loss of state information is to save and restore it. This behavior must be explicitly implemented by app developers, who often miss to implement it properly, releasing apps affected by data loss problems, that is, apps that may lose state information when their execution is interrupted. Although several techniques can be used to automatically generate test cases for Android apps, the obtained test cases seldom include the interactions and the checks necessary to exercise and reveal data loss faults. To address this problem, this paper presents Data Loss Detector (DLD), a test case generation technique that integrates an exploration strategy, data-loss-revealing actions, and two customized oracle strategies for the detection of data loss failures. DLD revealed 75% of the faults in a benchmark of 54 Android app releases affected by 110 known data loss faults, and also revealed unknown data loss problems, outperforming competing approaches.