Transform coding plays a crucial role in video coders. Recently, additional transforms based on the DST and the DCT have been included in the latest video coding standard, HEVC. Those transforms were introduced after a thoroughly analysis of the video signal properties. In this paper, we design additional transforms by using an alternative learning approach. The appropriateness of the design over the classical KLT learning is also shown. Subsequently, the additional designed transforms are applied to the latest HEVC scheme. Results show that coding performance is improved compared to the standard. Additional results show that the coding performance can be significantly further improved by using non-separable transforms. Bit-rate reductions in the range of 2% over HEVC are achieved with those proposed transforms.
State of the art video coders are based on prediction and transform coding. The transform decorrelates the signal to achieve high compression levels. In this paper we propose improving the performances of the latest video coding standard, HEVC, by adding a set of rate-distortion optimised transforms (RDOTs). The transform design is based upon a cost function that incorporates a bit rate constraint. These new RDOTs compete against classical HEVC transforms in the rate-distortion optimisation (RDO) loop in the same way as prediction modes and block sizes, providing additional coding possibilities. Reductions in BD-rate of around 2% are demonstrated when making these transforms available in HEVC.
International audienceTransform coding plays a key role in state-of-the-art video coders, such as HEVC. However, transforms used in current solutions do not cover the varieties of video coding signals. This work presents an adaptive transform design method that enables the use of multiple transforms in HEVC. A different transform set is learnt for each intra prediction mode, allowing the video encoder to perform better decisions regarding block sizes, prediction modes and transforms. Different systems are proposed to accommodate trade-offs between complexity and performance. Bit rate reductions in the range of 2% to 7% are reported, depending on complexity
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.