US 12,306,583 B2
Holographic display method and holographic display system
Jinfeng Liu, Guangdong (CN)
Assigned to TCL China Star Optoelectronics Technology Co., Ltd., Shenzhen (CN)
Appl. No. 17/430,404
Filed by TCL China Star Optoelectronics Technology Co., Ltd., Guangdong (CN)
PCT Filed May 31, 2021, PCT No. PCT/CN2021/097499
§ 371(c)(1), (2) Date Aug. 12, 2021,
PCT Pub. No. WO2022/147956, PCT Pub. Date Jul. 14, 2022.
Claims priority of application No. 202110018233.4 (CN), filed on Jan. 7, 2021.
Prior Publication US 2023/0134309 A1, May 4, 2023
Int. Cl. G03H 1/00 (2006.01); G02B 27/00 (2006.01); G02F 1/1335 (2006.01); G03H 1/22 (2006.01)
CPC G03H 1/0005 (2013.01) [G02B 27/0093 (2013.01); G02F 1/133524 (2013.01); G02F 1/133623 (2021.01); G03H 1/2294 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A holographic display method, comprising following steps:
S10, obtaining target scene information;
S20, recognizing human face area information of the target scene information;
S30, confirming a pupil position of the human face area information;
S40, calculating a lateral viewing angle between each pixel area and each pupil position in a display panel and a driving voltage value of each pixel area at the lateral viewing angle corresponding to each pixel area; and
S50, applying the driving voltage value to the corresponding pixel area;
wherein the S20 further comprises following steps:
S201, training a human face area detecting module; and
S202, recognizing the human face area information of the target scene information by the human face area detecting module;
wherein the S201 further comprises:
S2011, providing an original image data set including a human face image to a feature extraction network, wherein the feature extraction network outputs original image feature information; and
S2022, respectively inputting the original image feature information into an area extraction network and a classification and position feedback network, wherein the area extraction network is configured to extract a histogram of oriented gradient (HOG) feature of a predetermined area, and the classification and position feedback network is configured to classify the HOG feature and output the human face area information;
wherein in the S2022, in the area extraction network, using a plurality of sliding windows with different sizes to encircle parts of an area having the original image feature information as a selected area and creating a region of interests (ROIs), wherein the ROIs is configured to extract the HOG feature of the selected area and output the HOG feature to the classification and position feedback network;
wherein the step of creating the ROIs further comprises following steps:
grouping each sub-pixel of parts of the selected area of the area having the original image feature information;
calculating a texture feature of each group; and
combining the texture feature with two groups of the sub-pixel which are the most similar to each other, wherein the ROIs is finally obtained after all sub-pixels in a certain space are combined with each other.