Recently, video compression gained a large focus among computer vision problems in media technologies. Using state of the art video compression methods, videos can be transmitted in a better quality requiring less bandwidth and memory. The advent of neural network-based video compression methods remarkably promoted video coding performance. In this paper, a video compression method is presented based on Recurrent Neural Network (RNN). The method includes an encoder, a middle module, and a decoder. Binarizer is utilized in the middle module to achieve better quantization performance. In encoder and decoder modules, long short-term memory (LSTM) units are used to keep the valuable information and eliminate unnecessary ones to iteratively reduce the quality loss of reconstructed video. This method reduces the complexity of neural network-based compression schemes and encodes the videos with less quality loss. The proposed method is evaluated using peak signal-to-noise ratio (PSNR), video multimethod assessment fusion (VMAF), and structural similarity index measure (SSIM) quality metrics. The proposed method is applied to two different public video compression datasets and the results show that the method outperforms existing standard video encoding schemes such as H.264 and H.265.

In this paper, we propose a novel framework for replacing advertisement contents in soccer videos with an automatic way by using deep learning strategies. We begin by applying UNET (an image segmentation convolutional neural network technique) for content segmentation and detection. Subsequently, after reconstructing the segmented content in the video frames (considering the apparent loss in detection), we will replace the unwanted content by new one using a homography mapping procedure. Furthermore, the replacement key points in each frame will be tracked into the next frames considering the camera zoom-in and zoom-out controlling. Since the movement of objects in video can disrupt the alignment between frames and correspondingly make the homography matrix calculation erroneous, we use Mask R-CNN to mask and remove the moving objects from the scene. Such framework is denominated as REP-Model which stands for a replacing model.

Along with the exponential growth of online video creation platforms such as Tik Tok and Instagram, state of the art research involving quick and effective action/gesture recognition applications remains crucial. This work addresses the challenge of classifying such short video clips, using a domain-specific feature design approach, capable of performing significantly well using little training data. The method is based on Gunner Farneback dense optical flow (GF-OF) estimation strategy, Gaussian mixture models, and information divergence. We first aim to obtain accurate 3D representations of the human movements/actions through clustering the results given by GF-OF using K-means method of vector quantization. We then proceed by representing the result of one instance of each action by a Gaussian mixture model. Furthermore, using Kullback-Leibler divergence (KL-divergence), we attempt to find similarities between the trained actions and the ones in the test videos. Classification is done by matching each testing video to the trained action with the highest similarity (lowest KL-divergence). We have performed experiments on the KTH and Weizmann Human Action datasets, and the results reveal the discriminative nature of our proposed methodology in comparison with other state of the art techniques.

The light field information would be captured through light field cameras and in different directions regarding 3D image view recordings. In this paper, in order to increase the spatio-angular super-resolution quality and to decrease the reconstruction error regarding the light field information, we use a graph-based light field super-resolution strategy. Accordingly, in order to apply the complementary data in the light field views, we use a graph regularizer for the total recovery of the information and an edge-preserving technique that represents an isometry between curves in the 2D manifold and 5D space of the RGB image views. Moreover, the reconstruction of the light field information is based on applying the alternating direction method of multipliers (ADMM) algorithm. Accordingly, a recent enhanced ADMM model has been used in this paper which is denominated as “Plug-and-Play” and permits the user to plug an image reconstruction technique and a denoising methodology as the first and second sub-problems respectively. On that account, we would be able to resolve the light field super-resolution problem considering the graph-based light field structure as the first sub-problem and the edge-preserving technique as the denoising methodology. Consequently, by applying the proposed super-resolution strategy, the super-resolved light field outcome would be more favorable in terms of visual quality and reconstruction errors in comparison with other state-of-the-art methodologies.

Tremor extraction techniques are considered as the central component of several rehabilitative and compensatory robotic technologies, and the accuracy of such filters can directly affect the performance of the aforementioned technologies. Motivated by this fact, the paper proposes an adaptive estimation framework, referred to as Multiple Adaptive Reduced-order Kalman filtering (KFE-BMFLC), for extraction of pathological hand tremors. The proposed KFE-BMFLC framework is designed with the goal of improving the performance of an existing state-of-the-art filtering technique, i.e. Enhanced Band-limited Fourier Linear Combiner (E-BMFLC), which has shown a promising potential in extracting involuntary hand motions but uses embedded least mean square (LMS) estimation approach. The proposed technique is capable of reducing the computational overhead in comparison to that of the conventional BMFLC technique, while increasing the estimation accuracy.