Sai Deng

Bio: Sai Deng is an academic researcher from Google. The author has contributed to research in topics: Video quality & Codec. The author has an hindex of 3, co-authored 5 publications receiving 39 citations. Previous affiliations of Sai Deng include University of Utah.

A Technical Overview of AV1

Abstract: The AV1 video compression format is developed by the Alliance for Open Media consortium. It achieves more than a 30% reduction in bit rate compared to its predecessor VP9 for the same decoded video quality. This article provides a technical overview of the AV1 codec design that enables the compression performance gains with considerations for hardware feasibility.

Structured Volume Decomposition via Generalized Sweeping.

Abstract: In this paper, we introduce a volumetric partitioning strategy based on a generalized sweeping framework to seamlessly partition the volume of an input triangle mesh into a collection of deformed cuboids. This is achieved by a user-designed volumetric harmonic function that guides the decomposition of the input volume into a sequence of two-manifold level sets. A skeletal structure whose corners correspond to corner vertices of a 2D parameterization is extracted for each level set. Corners are placed so that the skeletal structure aligns with features of the input object. Then, a skeletal surface is constructed by matching the skeletal structures of adjacent level sets. The surface sheets of this skeletal surface partition the input volume into the deformed cuboids. The collection of cuboids does not exhibit T-junctions, significantly simplifying the hexahedral mesh generation process, and in particular, it simplifies fitting trivariate B-splines to the deformed cuboids. Intersections of the surface sheets of the skeletal surface correspond to the singular edges of the generated hex-meshes. We apply our technique to a variety of 3D objects and demonstrate the benefit of the structure decomposition in data fitting.

VMAF Based Rate-Distortion Optimization for Video Coding

Abstract: Video Multi-method Assessment Fusion (VMAF) is a machine-learning based video quality metric. It is experimentally shown to provide higher correlation with human visual system as compared to conventional metrics like peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) in many scenarios and has drawn considerable interest as an alternative metric to evaluate the perceptual quality. This work proposes a systematic approach to improve the video compression performance in VMAF. It is composed of multiple components including a pre-processing stage with a complement automatic filter parameter selection, and a modified rate-distortion optimization framework tailored for VMAF metric. The proposed scheme achieves on average 37% BD-rate reduction in VMAF, as compared to conventional video codec optimized for PSNR.

A Technical Overview of AV1

Abstract: The AV1 video compression format is developed by the Alliance for Open Media consortium. It achieves more than 30% reduction in bit-rate compared to its predecessor VP9 for the same decoded video quality. This paper provides a technical overview of the AV1 codec design that enables the compression performance gains with considerations for hardware feasibility.

Machine Learning Based Symbol Probability Distribution Prediction For Entropy Coding In Av1

Abstract: Entropy coding is a lossless data compression technique that is widely applied in video codecs to encode syntax elements into bitstreams. Efficient entropy coding requires accurate prediction of the probability distribution of the encoded symbols. In AV1, multi-symbol arithmetic coding is adopted. The symbol probability is derived with handcrafted context models and lookup tables that store the predicted probabilities corresponding to different entropy contexts. The lookup table based scheme has some fundamental deficiencies. The entropy context features have to be discrete so that they can be used to index the lookup tables. To reduce the size of the lookup table, the number of contexts cannot be very large. Moreover, the probability distributions stored in the lookup tables are maintained separately without taking their correlations into consideration. In this paper, we propose a machine learning based scheme that achieves more accurate symbol probability prediction for entropy coding. The proposed approach is implemented in AV1 for the entropy coding of intra prediction modes. Experimental results demonstrate that it can improve the efficiency of entropy coding significantly.

구글 TensorFlow 소개

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On discrete cosine transform

Abstract: The discrete cosine transform (DCT), introduced by Ahmed, Natarajan and Rao, has been used in many applications of digital signal processing, data compression and information hiding. There are four types of the discrete cosine transform. In simulating the discrete cosine transform, we propose a generalized discrete cosine transform with three parameters, and prove its orthogonality for some new cases. A new type of discrete cosine transform is proposed and its orthogonality is proved. Finally, we propose a generalized discrete W transform with three parameters, and prove its orthogonality for some new cases. Keywords: Discrete Fourier transform, discrete sine transform, discrete cosine transform, discrete W transform Nigerian Journal of Technological Research , vol7(1) 2012

Skeleton-driven adaptive hexahedral meshing of tubular shapes

Abstract: We propose a novel method for the automatic generation of structured hexahedral meshes of articulated 3D shapes We recast the complex problem of generating the connectivity of a hexahedral mesh of a general shape into the simpler problem of generating the connectivity of a tubular structure derived from its curve-skeleton We also provide volumetric subdivision schemes to nicely adapt the topology of the mesh to the local thickness of tubes, while regularizing per-element size Our method is fast, one-click, easy to reproduce, and it generates structured meshes that better align to the branching structure of the input shape if compared to previous methods for hexa mesh generation

Robust structure simplification for hex re-meshing

Abstract: We introduce a robust and automatic algorithm to simplify the structure and reduce the singularities of a hexahedral mesh. Our algorithm interleaves simplification operations to collapse sheets and chords of the base complex of the input mesh with a geometric optimization, which improves the elements quality. All our operations are guaranteed not to introduce elements with negative Jacobians, ensuring that our algorithm always produces valid hex-meshes, and not to increase the Hausdorff distance from the original shape more than a user-defined threshold, ensuring a faithful approximation of the input geometry. Our algorithm can improve meshes produced with any existing hexahedral meshing algorithm --- we demonstrate its effectiveness by processing a dataset of 194 hex-meshes created with octree-based, polycube-based, and field-aligned methods.

HexaLab.net: An online viewer for hexahedral meshes

Abstract: We introduce HexaLab: a WebGL application for real time visualization, exploration and assessment of hexahedral meshes. HexaLab can be used by simply opening www.hexalab.net . Our visualization tool targets both users and scholars. Practitioners who employ hexmeshes for Finite Element Analysis, can readily check mesh quality and assess its usability for simulation. Researchers involved in mesh generation may use HexaLab to perform a detailed analysis of the mesh structure, isolating weak points and testing new solutions to improve on the state of the art and generate high quality images. To this end, we support a wide variety of visualization and volume inspection tools. Our system offers also immediate access to a repository containing all the publicly available meshes produced with the most recent techniques for hexmesh generation. We believe HexaLab, providing a common tool for visualizing, assessing and distributing results, will push forward the recent strive for replicability in our scientific community.