Review of Omnimedia Content Quality Evaluation

‍ ‍In the era of all media， the forms of media content are gradually enriched and become an important factor affecting the dissemination of information. Building models to assess the quality of omnimedia content has attracted increasing attention.Quality assessment methods are mainly divided into subjective models and objective models. Subjective methods aim to assess quality through human eyes and human senses.It requires a lot of manpower and material resources， and the evaluation process also takes a lot of time； therefore it is difficult to apply in practical application. Objective quality assessment simulates the human observation process， which can automatically predict quality input. This review mainly summarizes the evaluation models of different media published at home and abroad in the past ten years. Research work and corresponding applications in omnimedia data. We mainly list some influential methods in the two major directions based on traditional methods and methods based on deep learning. The quality assessment models of the video and audio parts are divided into traditional methods and deep learning-based models. Each type of model is divided into reference models and non-reference models. Compared with the methods with reference data， the performance of the non-reference method has some differences. However， the no-reference quality evaluation model has strong applicability because it does not need to rely on reference information， and has always been a research hotspot in the field of image quality evaluation. The image part is mainly developed by dividing the unreferenced quality assessment model into supervised learning and unsupervised learning. The unsupervised method does not require the support of manual scoring data， saves labor， and has good development prospects. The text quality assessment model is introduced from the two directions of automatic scoring system and text generation quality assessment. Finally， it is concluded that traditional or applied deep learning methods have their own characteristics. These methods are independent of each other and form their own systems. It also looks forward to the possible development direction of all-media content quality assessment in the future.