Tone-mapping operator (TMO) is intended to convert high dynamic range (HDR) content into a lower dynamic range so that it can be displayed on a standard dynamic range (SDR) device. The tonemapped result of HDR content is usually stored as SDR image. For different HDR scenes, traditional TMOs are able to obtain a satisfying SDR image only under manually fine-tuned parameters. In this paper, we address this problem by proposing a learning-based TMO using deep convolutional neural network (CNN). We explore different CNN structure and adopt multi-scale and multi-branch fully convolutional design. When training deep CNN, we introduce image quality assessments (IQA), specifically, tone-mapped image quality assessment, and implement it as semi-supervised loss terms. We discuss and prove the effectiveness of semisupervised loss terms, CNN structure, data pre-processing, etc. by several experiments. Finally, we demonstrate that our approach can produce appealing results under diversified HDR scenes.

In this paper, we present a novel no-reference (NR) model for perceptual video quality assessment, which can make quality prediction for high definition (HD) videos. This model is based on an artificial neural network (ANN) implemented by the back-propagation algorithm (BP), named as BP-ANN. Six video features are extracted from temporal and spatial domains as the input vectors. Subjective assessments are carried out by using double stimulus continuous quality scales (DSCQS) as the mean opinion scores (MOS), which are desired responses to the output layer. We establish a sample database to store all the videos, feature vectors and its corresponding MOS. Due to the combination of chrome features incorporated with a good use of regions of interest (ROI), our model can achieve good performance for the video quality prediction.

Along with the prosperity of streaming media business such as OTT (Over the Top) and Live Social Video Broadcasting Platform in recent years, the research on the streaming media processing technique has become a hot issue. This paper makes comprehensive specification of the RTMP (Real Time Message Protocol) in Adobe's Flash streaming media system, and introduces the implementation of software that can download RTMP-based streaming media. The software follows the RTMP specification, implements the connection with the server using Socket API in C language, processes multimedia data coming from server and saves it as a FLV (Flash Video) format file. The method mentioned in the structure of the software, can be used as the basis for the development of a more complete RTMP-based streaming media processing software.

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