Efficient Region-Wise Packing of Stereoscopic ERP Videos Based on Information Loss Minimization

Utilizing frame-compatible (FC) formats for packing stereoscopic videos often comes with challenges, as they require higher transmission bandwidth and larger memory buffers on the decoder compared to single-view videos. When it comes to stereoscopic 360° videos, as the primary content consumed by virtual reality (VR) applications, these requirements become even more challenging since they ask for ultra-high-resolution formats with high frame rates (e.g., 6K, 8K, or 12K at 100 frames per second). To address these challenges, sub-sampled versions of the left and right views are usually used to form the spatial FC format, leading to a loss of visual quality. In this paper, we propose an efficient region-wise packing method for equirectangular projection (ERP) videos with minimum information loss by exploiting the uneven sampling characteristic of ERP. Moreover, we propose a content-adaptive (CA) packing method for ERP videos, where the sizes of partitions, each with a particular horizontal downsampling factor, are adaptively determined based on spatial complexity. We then utilize a low-complexity frequency-domain approach to estimate the optimal partition sizes of the CA packing. We use these proposed methods to determine optimal packing of the stereoscopic ERP videos in the FC format. Experimental results, using the VVenC Versatile Video Coding (VVC) encoder, show that compared with the standard side-by-side (SbS) format, with uniform horizontal half-downsampling (UHHDS), the proposed CA packing method provides an average 13.84% and 12.02% Bjøntegaard-Delta bitrate (BD-BR) reduction for Random Access (RA) and Low Delay B (LDB) configurations, respectively, with an average encoding time comparable to SbS. In addition, when the performance is measured based on user attention probability, using the Laplacian Distribution model, the coding performance of our proposed packing methods outperforms the state-of-the-art packing method with significantly lower computational complexity.