公开(公告)号：US20180357744A1

公开(公告)日：2018-12-13

申请号：US15780052

申请日：2016-12-16

Applicant: STC.UNM

Inventor： Marios Stephanou PATTICHIS ,  Cesar CARRANZA ,  Daniel LLAMOCCA OBREGON

IPC: G06T1/20 ,  G06K9/52 ,  G06K9/46 ,  G06K9/00

CPC classification number: G06T1/20 ,  G06F17/153 ,  G06K9/00986 ,  G06K9/4642 ,  G06K9/522 ,  G06T2210/52

Abstract: Fast and scalable architectures and methods adaptable to available resources, that (1) compute 2-D convolutions using 1-D convolutions, (2) provide fast transposition and accumulation of results for computing fast cross-correlations or 2-D convolutions, and (3) provide parallel computations using pipelined 1-D convolvers. Additionally, fast and scalable architectures and methods that compute 2-D linear convolutions using Discrete Periodic Radon Transforms (DPRTs) including the use of scalable DPRT, Fast DPRT, and fast 1-D convolutions.

公开(公告)号：US20180357745A1

公开(公告)日：2018-12-13

申请号：US15780076

申请日：2016-12-16

Applicant: STC.UNM

Inventor： Marios Stephanou PATTICHIS ,  Cesar CARRANZA ,  Daniel LLAMOCCA OBREGON

IPC: G06T1/20 ,  G06F9/30 ,  G06T11/00

CPC classification number: G06F9/5066 ,  G01V2210/46 ,  G06T11/006

Abstract: Fast and a scalable algorithms and methods adaptable to available resources for computing (1) the DPRT on multicore CPUs by distributing the computation of the DPRT primary directions among the different cores, and (2) the DPRT on GPUs using parallel, distributed, and synchronized ray computations among the GPU cores with “ray” referring to one of the sums required for computing the DPRT or its inverse along a prime direction.

公开(公告)号：US20180220133A1

公开(公告)日：2018-08-02

申请号：US15747982

申请日：2016-07-31

Applicant: STC.UNM

Inventor： Marios Stephanou PATTICHIS ,  Yuebing JIANG ,  Cong ZONG ,  Gangadharan ESAKKI ,  Venkatesh JATLA ,  Andreas PANAYIDES

IPC: H04N19/127 ,  H04N19/126 ,  H04N19/147

CPC classification number: H04N19/127 ,  H04N19/119 ,  H04N19/126 ,  H04N19/147 ,  H04N19/149 ,  H04N19/172

Abstract: System and methods for the joint control of reconstructed video quality, computational complexity and compression rate for intra-mode and inter-mode video encoding in HEVC. The invention provides effective methods for (i) generating a Pareto front for intra-coding by varying CTU parameters and the QP, (ii) generating a Pareto front for inter-coding by varying GOP configurations and the QP, (iii) real-time and offline Pareto model front estimation using regression methods, (iv) determining the optimal encoding configurations based on the Pareto model by root finding and local search, and (v) robust adaptation of the constraints and model updates at both the CTU and GOP levels.