This paper introduces a new high-definition video quality database, referred to as BVI-HD, which contains 32 reference and 384 distorted video sequences plus subjective scores. The reference material in this database was carefully selected to optimize the coverage range and distribution uniformity of five low-level video features, while the included 12 distortions, using both original high efficiency video coding (HEVC) and HEVC with synthesis mode, represent state-of-the-art approaches to compression. The range of quantization parameters included in the database for HEVC compression was determined by a subjective study, the results of which indicate that a wider range of QP values should be used than the current recommendation. The subjective opinion scores for all 384 distorted videos were collected from a total of 86 subjects, using a double stimulus test methodology. Based on these results, we compare the subjective quality between HEVC and synthesised content, and evaluate the performance of nine state-of-the-art, full-reference objective quality metrics. This database has now been made available online, representing a valuable resource to those concerned with compression performance evaluation and objective video quality assessment.

Video databases, used for benchmarking and evaluating the performance of new video technologies, should represent the full breadth of consumer video content. The parameterisation of video databases using low-level features has proven to be an effective way of quantifying the diversity within a database. However, without a comprehensive understanding of the importance and relative frequency and of these features in the content people actually consume, the utility of such information is limited. Here, we present a large-scale analysis of programming on BBC One and CBeebies, the most popular television channels in the United Kingdom for adults and children, respectively. Twenty video features are extracted from almost three thousand television programmes shown throughout 2015 before principal components analysis is used to identify just five factors representing the most variation. The meaning and relative significance of these five factors together with the shape of their frequency distributions represent highly valuable information for researchers wanting to model the diversity of modern consumer content in representative video databases.

Subjective quality assessment is an essential component of modern image and video processing for both the validation of objective metrics and the comparison of coding methods. However, the standard procedures used to collect data can be prohibitively time consuming. One way of increasing the efficiency of data collection is to reduce the duration of test sequences from a 10-s length currently used in most subjective video quality assessment (VQA) experiments. Here, we explore the impact of reducing sequence length upon perceptual accuracy when identifying compression artifacts. A group of four reference sequences, together with five levels of distortion, are used to compare the subjective ratings of viewers watching videos between 1.5 and 10 s long. We identify a smooth function indicating that accuracy increases linearly as the length of the sequences increases from 1.5 to 7 s. The accuracy of observers viewing 1.5-s sequences was significantly inferior to those viewing sequences of 5, 7, and 10 s. We argue that sequences between 5 and 10 s produce satisfactory levels of accuracy but the practical benefits of acquiring more data lead us to recommend the use of 5-s sequences for future VQA studies that use the double stimulus continuous quality scale methodology.

A means for establishing transformation-invariant representations of objects is proposed and analyzed, in which different views are associated on the basis of the temporal order of the presentation of these views, as well as their spatial similarity. Assuming knowledge of the distribution of presentation times, an optimal linear learning rule is derived. Simulations of a competitive network trained on a character recognition task are then used t highlight the success of this learning rule in relation to simple Hebbian learning and to show that the theory can give accurate quantitative predictions for the optimal parameters for such networks.