Patent SG-11201808807W-A (SG)
Title: Techniques for robustly predicting perceptual video quality
Inventor: AARON ANNE
IPC: H04N19/00
Filed: 2017-04-03
Abstract: INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization 111111110111101110101011111010111110111010111111101101111011101111101111011111 International Bureau ... .... ..Yjd (10) International Publication Number (43) International Publication Date ...... ...r ....1 WO 2017/176656 Al 12 October 2017(12.10.2017) WIPO I PCT — (51) (21) (22) (25) Filing Language: (26) Publication Language: (30) (71) (72) International Patent Classification: (74) Agents: CAREY, John C. et al.; Artegis Law Group, LLP, H04N 19/00 (2014.01) 7710 Cherry Park Drive, Suite T104, Houston, Texas 77095 (US). International Application Number: PCT/US2017/025801 (81) Designated States (unless otherwise indicated, for every kind of national protection available): AE, AG, AL, AM, International Filing Date: AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, 3 April 2017 (03.04.2017) BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, English DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JP, KE, KG, KH, KN, English KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, Priority Data: MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, 62/319,688 7 April 2016 (07.04.2016) US NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, 15/207,468 11 July 2016 (11.07.2016) US RU, RW, SA, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, Applicant: NETFLIX, INC. [US/US]; 100 Winchester ZA, ZM, ZW. Circle, Los Gatos, California 95032 (US). (84) Designated States (unless otherwise indicated, for every Inventors: AARON, Anne; 100 Winchester Circle, Los kind of regional protection available): ARIPO (BW, GH, Gatos, California 95032 (US). LI, Zhi; 100 Winchester GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, Circle, Los Gatos, California 95032 (US). GOODALL, TZ, UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, Todd; 100 Winchester Circle, Los Gatos, California 95032 TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, (US). DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, [Continued on next page] = — (54) Title: TECHNIQUES FOR ROBUSTLY PREDICTING PERCEPTUAL VIDEO QUALITY (57) : In various embodiments, a quality trainer trains a model that A \ 500 = Receive genres obtecLve metnc values and subtectsre metnc values for baseline set computes a value for a perceptual video quality metric for encoded video 22 — During baseline 1 content. a pre-training phase, the quality trainer partitions = Generate folds based on genre values for metrics that describe baseline encoded video content into parti- 504 based on genre. The = - utt tions quality trainer then performs cross-validation op - = Generate Nmsusarameter combinahons and seleM tirst hyperparameter combination erations on the partitions to optimize hyperparameters associated with the ,' — 1 model. Subsequently, during a training phase, the quality trainer performs = H For each fold, train model that includes g ===nation on fold training metro values to training operations on the model that includes the optimized hyperparamet- 508 based the baseline values for the to trained model. - _ _ 1 ers on metrics generate a For each fold apply tradel that includes selecGd hyperparameter combination and toldspects parameters to The trained model accurately tracks the video quality for the baseline en- told testing metric values M generate perceptual quality scores = coded video content. Further, because the cross-validation operations min- = 1 Compute hyperparameter correlation based on scores and subtectrve metnc values imize any potential overfitting, the trained model accurately and consist- 12 5 ently predicts perceived video quality for non-baseline encoded video con- . - Last tent across a wide range of genres. hyperparameter 4 - Select neat hypemarameter , ombmn 515 = combinaLono 04 Selects i r I I on hypemarometer correlatons i t = Train model that includes the ophmized hyriemarameters based on the subject]. and object. metno values associated with entire baseline set to generate °Owed parameters - = Il .4 4:D ir) 4 :D 4 :D I...' 11 ..„.... I ... ' r 11 N Generate trained perceptual quality model that includes Me optimized Lyperparameters and the optimized parameters T zz Ire metric values for target encoded video 524 41 Apply trained perceptual quality model to obienve meat values to compute perceptual quality .ore FIGURE 5 O WO 2017/176656 Al MIDEDIM000101011101010M010111RIEM11111111111111011110111111 SM, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, Published: GW, KM, ML, MR, NE, SN, TD, TG). — with international search report (Art. 21(3))