The Konstanz natural video database (KoNViD-1k)

Subjective video quality assessment (VQA) strongly depends on semantics, context, and the types of visual distortions. Currently, all existing VQA databases include only a small number of video sequences with artificial distortions. The development and evaluation of objective quality assessment methods would benefit from having larger datasets of real-world video sequences with corresponding subjective mean opinion scores (MOS), in particular for deep learning purposes. In addition, the training and validation of any VQA method intended to be ‘general purpose’ requires a large dataset of video sequences that are representative of the whole spectrum of available video content and all types of distortions. We report our work on KoNViD-1k, a subjectively annotated VQA database consisting of 1,200 public-domain video sequences, fairly sampled from a large public video dataset, YFCC100m. We present the challenges and choices we have made in creating such a database aimed at ‘in the wild’ authentic distortions, depicting a wide variety of content.

I. Introduction

Most of the Internet traffic today stems from user-generated videos on sharing web-sites and social networks. Video sequences pass through several stages of processing before they reach consumers, which often deteriorate visual quality. Moreover, the vast amount of user-generated video content and the increased diversity of end user devices (ranging from smaller and power-constrained mobile devices to large displays such as 4K Ultra HDTVs and TV walls) calls for a broad range of video quality to be supported. Adapting video quality to different use cases has become an important topic for researchers, content providers and distributors [1].