Sorry, you need to enable JavaScript to visit this website.

In this paper, we consider using a multiscale approach to reduce complexity while maintaining coding efficiency. Experimental results demonstrate a 5.4× reduction in MAC operations while achieving an average bit rate savings of 6.4% and 6.3% for all intra and random access coding, respectively, when compared to the evolving AV2 standard. Ablation studies are also provided and show that the approach achieves all but 0.2% of the coding efficiency of full resolution processing.

Deep variational autoencoders for image and video compression have gained significant attraction
in the recent years, due to their potential to offer competitive or better compression
rates compared to the decades long traditional codecs such as AVC, HEVC or VVC. However,
because of complexity and energy consumption, these approaches are still far away
from practical usage in industry. More recently, implicit neural representation (INR) based
codecs have emerged, and have lower complexity and energy usage to classical approaches at

Decoder-side motion vector refinement (DMVR) was adopted into versatile video coding (VVC) and later was further improved in the enhance compression model (ECM) to improve the inter prediction accuracy by refining the motion vectors (MVs) derived from previously coded blocks in merge mode based on bilateral matching. However, DMVR can only be applied to blocks coded with translational motion. Affine motion compensation as supported by VVC can capture more complex motion and thus increases inter prediction accuracy, but DMVR is not applied to blocks coded with affine motion in VVC.

This work extends the multiscale structure originally developed for point cloud geometry compression to point cloud attribute compression. To losslessly encode the attribute while maintaining a low bitrate, accurate probability prediction is critical. With this aim, we extensively exploit cross-scale, cross-group, and cross-color correlations of point cloud attribute to ensure accurate probability estimation and thus high coding efficiency.

Decoder-side intra mode derivation (DIMD) is a promising coding tool in the enhanced compression model (ECM) developed by the joint video experts team (JVET). In DIMD, the intra prediction mode of a luma block is derived based on the gradient information of the adjacent luma samples at both encoder and decoder, rather than being explicitly signaled in the bitstream. Inspired by DIMD, a decoder-side chroma intra mode derivation (DCIMD) method is proposed in this paper to improve the coding efficiency of chroma intra prediction.