公开(公告)号：US20160286083A1

公开(公告)日：2016-09-29

申请号：US14664335

申请日：2015-03-20

Applicant: KYOCERA Document Solutions Inc.

Inventor： Dongpei Su ,  Kenneth A. Schmidt ,  Thien-Phuc Nguyen Do ,  Sheng Li

IPC: H04N1/46 ,  H04N19/129 ,  H04N19/176 ,  H04N1/411 ,  H04N19/124

CPC classification number: H04N1/4115 ,  H04N19/124 ,  H04N19/129 ,  H04N19/176 ,  H04N19/91 ,  H04N2201/0094

Abstract: A computer-implemented method comprises receiving a data stream that includes a series of code words that encodes a respective series of pixel data according to a first entropy coding lookup table, and processing the data stream to determine if there is a match between a first code word and a consecutive second code word, and a code word entry in a second entropy coding lookup table. The method also includes, if there is a match, decoding the first code word and the second code word using the second entropy coding lookup table. Further, the method includes, if there is not a match, decoding the first code word using the first entropy coding lookup table.

Abstract translation: 计算机实现的方法包括：接收包括根据第一熵编码查找表对各个像素数据序列进行编码的一系列码字的数据流，并处理数据流以确定第一代码 字和连续的第二码字，以及第二熵编码查找表中的码字条目。 该方法还包括如果存在匹配，则使用第二熵编码查找表对第一码字和第二码字进行解码。 此外，该方法包括如果不匹配，则使用第一熵编码查找表对第一码字进行解码。

公开(公告)号：US09380304B1

公开(公告)日：2016-06-28

申请号：US14610101

申请日：2015-01-30

Applicant: KYOCERA Document Solutions Inc.

Inventor： Michael M. Chang ,  Kenneth A. Schmidt ,  Dongpei Su ,  Sheng Li ,  Kendrick Wong

IPC: G06F15/00 ,  H04N19/103 ,  H04N1/00 ,  H04N1/21 ,  H04N19/119 ,  H04N19/132 ,  H04N19/136 ,  H04N19/17 ,  H04N19/60

CPC classification number: H04N19/103 ,  H04N1/00005 ,  H04N1/21 ,  H04N1/415 ,  H04N1/52 ,  H04N1/642 ,  H04N19/119 ,  H04N19/12 ,  H04N19/132 ,  H04N19/136 ,  H04N19/14 ,  H04N19/17 ,  H04N19/176 ,  H04N19/186 ,  H04N19/60 ,  H04N19/625 ,  H04N19/93 ,  H04N2201/0094

Abstract: An example embodiment may involve obtaining an a×b pixel macro-cell from an input image. Pixels in the a×b pixel macro-cell may have respective pixel values and may be associated with respective tags. It may be determined whether at least e of the respective tags indicate that their associated pixels represent edges in the input image. Based on this determination, either a first encoding or a second encoding of the a×b pixel macro-cell may be selected. The first encoding may weigh pixels that represent edges in the input image heavier than pixels that do not represent edges in the input image, and the second encoding might not consider whether pixels represent edges. The selected encoding may be performed and written to a computer-readable output medium.

Abstract translation: 示例性实施例可以涉及从输入图像获得a×b像素宏小区。 a×b像素宏小区中的像素可以具有各自的像素值，并且可以与相应的标签相关联。 可以确定各个标签中的至少e是否指示其相关联的像素表示输入图像中的边缘。 基于该确定，可以选择a×b像素宏小区的第一编码或第二编码。 第一编码可以称重表示比输入图像中不表示边缘的像素重的输入图像中的边缘的像素，并且第二编码可能不考虑像素是否表示边缘。 可以执行所选择的编码并将其写入计算机可读输出介质。

公开(公告)号：US20250097368A1

公开(公告)日：2025-03-20

申请号：US18898257

申请日：2024-09-26

Applicant: KYOCERA Document Solutions, Inc.

Inventor： Michael M. Chang ,  Dongpei Su ,  Sheng Li ,  Kenneth Allen Schmidt

IPC: H04N1/60 ,  G06N3/04 ,  G06N3/08

Abstract: A device such as a color printer includes a main memory, a cache memory, and a convolutional neural network configured to convert pixels from a first color space to a second color space. The convolutional neural network is organized into execution-separable layers, and loaded one or more layer at a time (depending on cache size) from the main memory to the cache memory, whereby the pixels are processed through each of the layers in the cache memory, and layers that have completed processing are evicted to make room for caching next layer(s) of the network.

公开(公告)号：US12132879B1

公开(公告)日：2024-10-29

申请号：US18468961

申请日：2023-09-18

Applicant: KYOCERA Document Solutions, Inc.

Inventor： Michael M. Chang ,  Dongpei Su ,  Sheng Li ,  Kenneth Allen Schmidt

IPC: H04N1/60 ,  G06N3/04 ,  G06N3/08

CPC classification number: H04N1/6016 ,  G06N3/04 ,  G06N3/08

Abstract: A device such as a color printer includes a main memory, a cache memory, and a convolutional neural network configured to convert pixels from a first color space to a second color space. The convolutional neural network is organized into execution-separable layers, and loaded one or more layer at a time (depending on cache size) from the main memory to the cache memory, whereby the pixels are processed through each of the layers in the cache memory, and layers that have completed processing are evicted to make room for caching next layer(s) of the network.

公开(公告)号：US12033303B2

公开(公告)日：2024-07-09

申请号：US17666917

申请日：2022-02-08

Applicant: KYOCERA Document Solutions, Inc.

Inventor： Sheng Li

IPC: G06T3/40 ,  G06T3/4046 ,  G06T3/4069 ,  G06V10/56 ,  G06V10/762 ,  G06V10/764

CPC classification number: G06T3/4069 ,  G06T3/4046 ,  G06V10/56 ,  G06V10/762 ,  G06V10/764

Abstract: A system for super-sampling digital images detects artifacts in an SRGAN super-sampled image, determines blocks of the image that contribute to the artifacts, and if the artifact-contributing blocks exceed a threshold, discards the SRGAN generated output image in favor of applying a super-sampled image generated by an alternate mechanism, such as a nearest neighbor algorithm.

公开(公告)号：US11769226B2

公开(公告)日：2023-09-26

申请号：US17158553

申请日：2021-01-26

Applicant: KYOCERA Document Solutions Inc.

Inventor： Sheng Li ,  Dongpei Su

IPC: G06K9/00 ,  G06K9/32 ,  G06T3/40 ,  G06T1/20

CPC classification number: G06T3/4046 ,  G06T1/20 ,  G06T3/4053 ,  G06T3/4084

Abstract: Systems and methods upscale an input image by a final upscaling factor. The systems and methods employ a first module implementing a super resolution neural network with feature extraction layers and multiple sets of upscaling layers sharing the feature extraction layers. The multiple sets of upscaling layers upscale the input image according to different respective upscaling factors to produce respective first module outputs. The systems and methods select the first module output with the respective upscaling factor closest to the final upscaling factor. If the respective upscaling factor for the selected first module output is equal to the final upscaling factor, the systems and methods output the selected first module output. Otherwise, the systems and methods provide the selected first module output to a second module that upscales the selected first module output to produce a second module output corresponding to the input image upscaled by the final upscaling factor.

公开(公告)号：US09363422B1

公开(公告)日：2016-06-07

申请号：US14610178

申请日：2015-01-30

Applicant: KYOCERA Document Solutions Inc.

Inventor： Michael M. Chang ,  Kenneth A. Schmidt ,  Dongpei Su ,  Sheng Li ,  Kendrick Wong ,  Larry Lubman

IPC: H04N1/64 ,  H04N1/58 ,  H04N1/50 ,  G06F3/12

CPC classification number: H04N1/64 ,  G06F3/1208 ,  G06F3/122 ,  G06F3/1229 ,  H04N1/50 ,  H04N1/52 ,  H04N1/58 ,  H04N1/642 ,  H04N1/644 ,  H04N2201/0094

Abstract: An example embodiment may involve obtaining (i) an a×b attribute macro-cell, and (ii) a×b pixel macro-cells for each of a luminance plane, a first color plane, and a second color plane of an input image. The a×b pixel macro-cells may each contain 4 non-overlapping m×n pixel cells. The example embodiment may also involve determining 4 attribute-plane output values that represent the 4 non-overlapping m×n attribute cells, 1 to 4 luminance-plane output values that represent the a×b pixel macro-cell of the luminance plane, a first color-plane output value to represent the a×b pixel macro-cell of the first color plane, and a second color-plane output value to represent the a×b pixel macro-cell of the second color plane. The example embodiment may further involve writing an interleaved representation of the output values to a computer-readable output medium.

Abstract translation: 示例性实施例可以包括获得（i）a×b属性宏小区，以及（ii）输入图像的亮度平面，第一彩色平面和第二彩色平面中的每一个的×b像素宏小区 。 a×b像素宏单元可以各自包含4个非重叠的m×n个像素单元。 示例性实施例还可以包括确定表示4个非重叠m×n个属性单元的4个属性平面输出值，表示亮度平面的a×b个像素宏小区的1到4个亮度平面输出值， 第一颜色平面输出值，以表示第一彩色平面的a×b像素宏小区，以及第二颜色平面输出值，以表示第二彩色平面的a×b像素宏小区。 示例实施例还可以包括将输出值的交织表示写入计算机可读输出介质。

公开(公告)号：US11909934B1

公开(公告)日：2024-02-20

申请号：US18055770

申请日：2022-11-15

Applicant: KYOCERA Document Solutions, Inc.

Inventor： Waqas Rasheed ,  Dongpei Su ,  Kilho Shin ,  Sheng Li

IPC: H04N1/00 ,  G06V30/413

CPC classification number: H04N1/00859 ,  G06V30/413

Abstract: A digital image processor includes a region proposal network configured to transform digital image inputs into region proposals and bounding box refinement logic configured to transform the region proposals by determining a first set of the region proposals exhibiting dense spacing, determining a second set of the region proposals exhibiting sparse spacing, executing a first transformation to merge at least some of the region proposals exhibiting dense spacing to generate refined region proposals, executing a second transformation to join at least some of the region proposals exhibiting sparse spacing to generate additional ones of the refined region proposals, and applying an expansion transformation to the refined region proposals.

公开(公告)号：US20230252603A1

公开(公告)日：2023-08-10

申请号：US17666917

申请日：2022-02-08

Applicant: KYOCERA Document Solutions, Inc.

Inventor： Sheng Li

IPC: G06T3/40 ,  G06V10/764 ,  G06V10/56 ,  G06V10/762

CPC classification number: G06T3/4069 ,  G06T3/4046 ,  G06V10/764 ,  G06V10/56 ,  G06V10/762

Abstract: A system for super-sampling digital images detects artifacts in an SRGAN super-sampled image, determines blocks of the image that contribute to the artifacts, and if the artifact-contributing blocks exceed a threshold, discards the SRGAN generated output image in favor of applying a super-sampled image generated by an alternate mechanism, such as a nearest neighbor algorithm.

公开(公告)号：US11508034B2

公开(公告)日：2022-11-22

申请号：US17157990

申请日：2021-01-25

Applicant: KYOCERA Document Solutions Inc.

Inventor： Sheng Li ,  Dongpei Su

IPC: G06T3/40 ,  G06T5/50 ,  G06T7/194 ,  G06T7/11 ,  G06T7/136

Abstract: Systems and methods for processing images receive an input image. The systems and methods provide the input image to a first module to increase a resolution of the input image to produce an upscaled image. The systems and methods detect white pixels in the input image. The systems and methods generate a mask associated with the input image. The mask includes mask bits that are set to mark the white pixels in the input image. The systems and methods upscale the mask to produce an upscaled mask matching a resolution of the upscaled image. The systems and methods identify target pixels of the upscaled image that correspond to the set mask bits in the upscaled mask. The systems and methods modify the upscaled image to produce an output image by replacing target pixels of the upscaled image with a replacement pixel having greater whiteness. The systems and methods output the output image.