Comparative Study of Dimensionality Reduction Techniques for Spectral–Temporal Data

This paper studies the use of three different approaches to reduce the dimensionality of a type of spectral–temporal features, called motion picture expert group (MPEG)-7 audio signature descriptors (ASD). The studied approaches include principal component analysis (PCA), independent component analysis (ICA), and factor analysis (FA). These approaches are applied to ASD features obtained from audio items with or without distortion. These low-dimensional features are used as queries to a dataset containing low-dimensional features extracted from undistorted items. Doing so, we may investigate the distortion-resistant capability of each approach. The experimental results show that features obtained by the ICA or FA reduction approaches have higher identification accuracy than the PCA approach for moderately distorted items. Therefore, to extract features from distorted items, ICA or FA approaches should also be considered in addition to the PCA approach.

Overview of HEVC extensions on screen content coding

Technologies for coding non-camera-captured video contents have received great interests lately due to the rapid growth of application areas such as wireless display and screen sharing, etc. In response to the market demands, the ITU-T Video Coding Expert Group and ISO/IEC Motion Picture Expert Group have jointly launched a new standardization project, i.e. the High-Efficiency Video Coding (HEVC) extensions on screen content coding (HEVC SCC). Several new video coding tools, including intra block copy, palette coding, adaptive color transform, and adaptive motion resolution, have been developed and adopted into HEVC SCC draft standard. This paper reviews the main features and coding technologies in the current HEVC SCC draft standard, with discussions about the performance and complexity aspects compared with prior arts.