236 research outputs found
Quality comparison of the HEVC and VP9 encoders performance
This paper reports a comparison between two recent video codecs, namely the HEVC and the VP9, using High Definition Video Sequences encoded with different bit rates. A subjective test for the evaluation of the provided Quality of Experience is reported. The video sequences were shown to a panel of subjects on a High Definition LED display and the subjective tests were performed using a Single Stimulus Methodology. The results shown that the HEVC encoder provides a better visual quality on low bit rates than the VP9. Similar performance was obtained for visually lossless conditions, although the HEVC requires lower bit rates to reach that level. Moreover, the correlation of the subjective evaluation and three tested objective metrics (PSNR, SSIM, and FSIM) revealed a good representation of the subjective results, particularly the SSIM and the FSIM metrics.info:eu-repo/semantics/publishedVersio
Depth map compression via 3D region-based representation
In 3D video, view synthesis is used to create new virtual views between
encoded camera views. Errors in the coding of the depth maps introduce
geometry inconsistencies in synthesized views. In this paper, a new 3D plane
representation of the scene is presented which improves the performance of
current standard video codecs in the view synthesis domain. Two image segmentation
algorithms are proposed for generating a color and depth segmentation.
Using both partitions, depth maps are segmented into regions without
sharp discontinuities without having to explicitly signal all depth edges. The
resulting regions are represented using a planar model in the 3D world scene.
This 3D representation allows an efficient encoding while preserving the 3D
characteristics of the scene. The 3D planes open up the possibility to code
multiview images with a unique representation.Postprint (author's final draft
Non-local Attention Optimized Deep Image Compression
This paper proposes a novel Non-Local Attention Optimized Deep Image
Compression (NLAIC) framework, which is built on top of the popular variational
auto-encoder (VAE) structure. Our NLAIC framework embeds non-local operations
in the encoders and decoders for both image and latent feature probability
information (known as hyperprior) to capture both local and global
correlations, and apply attention mechanism to generate masks that are used to
weigh the features for the image and hyperprior, which implicitly adapt bit
allocation for different features based on their importance. Furthermore, both
hyperpriors and spatial-channel neighbors of the latent features are used to
improve entropy coding. The proposed model outperforms the existing methods on
Kodak dataset, including learned (e.g., Balle2019, Balle2018) and conventional
(e.g., BPG, JPEG2000, JPEG) image compression methods, for both PSNR and
MS-SSIM distortion metrics
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