Hubert Koepfer

US Patent:

20140029675, Jan 30, 2014

Filed:

Apr 13, 2012

Appl. No.:

14/110694

Inventors:

Sheng Qu - San Jose CA, US
Hubert Koepfer - Milpitas CA, US
Yufei Yuan - Austin TX, US
Samir Hulyalkar - Los Gatos CA, US

Assignee:

DOLBY LABORATORIES LICENSING CORPORATION - San Francisco CA

International Classification:

H04N 7/26

US Classification:

37524016

Abstract:

Inter-color image prediction is based on multi-channel multiple regression (MMR) models. Image prediction is applied to the efficient coding of images and video signals of high dynamic range. MMR models may include first order parameters, second order parameters, and cross-pixel parameters. MMR models using extension parameters incorporating neighbor pixel relations are also presented. Using minimum means-square error criteria, closed form solutions for the prediction parameters are presented for a variety of MMR models.

US Patent:

20140037205, Feb 6, 2014

Filed:

Apr 13, 2012

Appl. No.:

14/110701

Inventors:

Sheng Qu - San Jose CA, US
Hubert Koepfer - Milpitas CA, US
Yufei Yuan - Austin TX, US
Samir Hulyalkar - Los Gatos CA, US
Walter Gish - Oak Park CA, US

Assignee:

DOLBY LABORATORIES LICENSING CORPORATION - San Francisco CA

International Classification:

H04N 7/26

US Classification:

382166, 382238

Abstract:

Inter-color image prediction is based on color grading modeling. Prediction is applied to the efficient coding of images and video signals of high dynamic range. Prediction models may include a color transformation matrix that models hue and saturation color changes and a non-linear function modeling color correction changes. Under the assumption that the color grading process uses a slope, offset, and power (SOP) operations, an example non linear prediction model is presented.

US Patent:

20130329778, Dec 12, 2013

Filed:

Jun 3, 2013

Appl. No.:

13/908926

Inventors:

Qian Chen - Santa Clara CA, US
Hubert Koepfer - Milpitas CA, US
Sheng Qu - San Jose CA, US

International Classification:

H04N 7/26

US Classification:

37524001

Abstract:

An encoder receives one or more input pictures of enhanced dynamic range (EDR) to be encoded in a coded bit stream comprising a base layer and one or more enhancement layer. The encoder comprises a base layer quantizer (BLQ) and an enhancement layer quantizer (ELQ) and selects parameters of the BLQ and the ELQ by a joint BLQ-ELQ adaptation method which given a plurality of candidate sets of parameters for the BLQ, for each candidate set, computes a joint BLQ-ELQ distortion value based on a BLQ distortion function, an ELQ distortion function, and at least in part on the number of input pixels to be quantized by the ELQ. The encoder selects as the output BLQ parameter set the candidate set for which the computed joint BLQ-ELQ distortion value is the smallest. Example ELQ, BLQ, and joint BLQ-ELQ distortion functions are provided.

US Patent:

20180278930, Sep 27, 2018

Filed:

May 24, 2018

Appl. No.:

15/988937

Inventors:

- San Francisco CA, US
Sheng QU - SAN JOSE CA, US
Hubert KOEPFER - MILPITAS CA, US
Yufei YUAN - AUSTIN TX, US
Samir HULYALKAR - LOS GATOS CA, US

Assignee:

DOLBY LABORATORIES LICENSING CORPORATION - SAN FRANCISCO CA

International Classification:

H04N 19/105
H04N 19/192
H04N 19/147
H04N 19/30
H04N 19/98
G06F 17/18
H04N 19/16

Abstract:

Inter-color image prediction is based on multi-channel multiple regression (MMR) models. Image prediction is applied to the efficient coding of images and video signals of high dynamic range. MMR models may include first order parameters, second order parameters, and cross-pixel parameters. MMR models using extension parameters incorporating neighbor pixel relations are also presented. Using minimum means-square error criteria, closed form solutions for the prediction parameters are presented for a variety of MMR models.

US Patent:

20170264898, Sep 14, 2017

Filed:

May 30, 2017

Appl. No.:

15/608433

Inventors:

- SAN FRANCISCO CA, US
Sheng QU - SAN JOSE CA, US
Hubert KOEPFER - MILPITAS CA, US
Yufei YUAN - AUSTIN TX, US
Samir HULYALKAR - LOS GATOS CA, US

International Classification:

H04N 19/105
H04N 19/98
G06F 17/18
H04N 19/192

Abstract:

Inter-color image prediction is based on multi-channel multiple regression (MMR) models. Image prediction is applied to the efficient coding of images and video signals of high dynamic range. MMR models may include first order parameters, second order parameters, and cross-pixel parameters. MMR models using extension parameters incorporating neighbor pixel relations are also presented. Using minimum means-square error criteria, closed form solutions for the prediction parameters are presented for a variety of MMR models.

US Patent:

20170019670, Jan 19, 2017

Filed:

Sep 29, 2016

Appl. No.:

15/280822

Inventors:

- San Francisco CA, US
Sheng QU - San Jose CA, US
Samir N. HULYALKAR - Los Gatos CA, US
Tao CHEN - Palo Alto CA, US
Walter C. GISH - Oak Park CA, US
Hubert KOEPFER - Milpitas CA, US

Assignee:

Dolby Laboratories Licensing Corporation - San Francisco CA

International Classification:

H04N 19/124
H04N 19/103

Abstract:

Techniques use multiple lower bit depth (e.g., 8 bits) codecs to provide higher bit depth (e.g., 12+ bits) high dynamic range images from an upstream device to a downstream device. Multiple layers comprising a base layer and one or more enhancement layers may be used to carry video signals comprising image data compressed by lower bit depth encoders to a downstream device, wherein the base layer cannot be decoded and viewed on its own. Lower bit depth input image data to base layer processing may be generated from higher bit depth high dynamic range input image data via advanced quantization to minimize the volume of image data to be carried by enhancement layer video signals. The image data in the enhancement layer video signals may comprise residual values, quantization parameters, and mapping parameters based in part on a prediction method corresponding to a specific method used in the advanced quantization. Adaptive dynamic range adaptation techniques take into consideration special transition effects, such as fade-in and fade-outs, for improved coding performance.

US Patent:

20160269756, Sep 15, 2016

Filed:

May 24, 2016

Appl. No.:

15/163613

Inventors:

- SAN FRANCISCO CA, US
Sheng QU - SAN JOSE CA, US
Hubert KOEPFER - MILPITAS CA, US
Yufei YUAN - AUSTIN TX, US
Samir HULYALKAR - LOS GATOS CA, US

International Classification:

H04N 19/98
H04N 19/30
H04N 19/192
H04N 19/16

Abstract:

Inter-color image prediction is based on multi-channel multiple regression (MMR) models. Image prediction is applied to the efficient coding of images and video signals of high dynamic range. MMR models may include first order parameters, second order parameters, and cross-pixel parameters. MMR models using extension parameters incorporating neighbor pixel relations are also presented. Using minimum means-square error criteria, closed form solutions for the prediction parameters are presented for a variety of MMR models.

US Patent:

20160014420, Jan 14, 2016

Filed:

Mar 25, 2014

Appl. No.:

14/771101

Inventors:

- San Francisco CA, US
Qian CHEN - San Jose CA, US
Hubert KOEPFER - Milpitas CA, US

Assignee:

DOLBY LABORATORIES LICENSING CORPORATION - San Francisco CA

International Classification:

H04N 19/176
H04N 19/70
H04N 19/44
H04N 19/50
H04N 19/136
H04N 19/30

Abstract:

Input VDR images are received. A candidate set of function parameter values for a mapping function is selected from multiple candidate sets. A set of image blocks of non-zero standard deviations in VDR code words in at least one input VDR image is constructed. Mapped code values are generated by applying the mapping function with the candidate set of function parameter values to VDR code words in the set of image blocks in the at least one input VDR image. Based on the mapped code values, a subset of image blocks of standard deviations below a threshold value in mapped code words is determined as a subset of the set of image blocks. Based at least in part on the subset of image blocks, it is determined whether the candidate set of function parameter values is optimal for the mapping function to map the at least one input VDR image.