In this paper, we present an experimental image quality assessment (IQA) method for image/ video patches with compression artifacts. Using the High Efficiency Video Coding (HEVC) standard, we create a new database of image patches with compression artifacts. Then, we conduct a completed subjective testing process to obtain the 'ground truth' quality scores for the mentioned database. Finally, we employ an end-to-end learning method to estimate the IQA model for the patches with HEVC compression artifacts. In such proposed method, a modified convolutional neural network (CNN) architecture is exploited for feature extraction while an adaptive moment estimation optimizer solution is used to perform the training process. Experimental results show that the proposed end-to-end IQA method significantly outperforms the relevant IQA benchmarks, especially when the compression artifacts are strongly realized in image/video patches. The proposed IQA method is expected to drive a new set of image/video compression solutions in future image/video coding and transmissions.
Versatile Video Coding (VVC) has been recently becoming popular in coding videos due to its compression efficiency. To reach this performance, Joint Video Experts Team (JVET) has introduced a number of improvement techniques to VVC coding model. Among them, VVC Intra coding introduces a new concept of quad-tree nested multi-type tree (QTMT) and extends the predicted modes with up to 67 options. As a result, the complexity of the VVC Intra encoding also greatly increases. To make VVC Intra coding more feasible in real-time applications, we propose in this paper a novel deep learning based fast QTMT and an early mode prediction method. At the first stage, we use a learned convolutional neural network (CNN) model to predict the coding unit map and then fed into the VVC encoder to early terminate the block partitioning process. After that, we design a statistical model to predict a list of most probable modes (MPM) for each selected Coding using (CU) size. Finally, we employ a so-called three-steps mode decision algorithm to estimate the optimal directional mode without sacrificing the compression performance. The proposed early CU splitting and fast intra prediction are integrated into the latest VTM reference software. Experimental results show that the proposed method can save 50.2% of encoding time with a negligible BD-Rate increase.
In this paper, we propose a novel multiple description coding (MDC) method, which offers benefits of the new H.265/HEVC video coding standard combined with path diversity systems for robust video transmissions. In the proposed method, two descriptions including odd and even video subsequences are encoded using H.265/HEVC coder and then transmitted over two distinct channels of a path diversity system. At the receiver, the proposed MDC decoder is designed using a novel concept of distributed video coding (DVC) to provide a high image quality for the reconstructed description. Experimental results show that the proposed method can achieve a wide range of tradeoffs between coding efficiency and error resilience, and provide much better H.265/HEVC quality of experiences (QoEs) for users than other conventional MDC methods results
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