Luma Adjustment for High Dynamic Range Video. Jacob Ström, Jonatan Samuelsson, Kristofer Dovstam Ericsson Research
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1 Luma Adjustment for High Dynamic Range Video Jacob Ström, Jonatan Samuelsson, Kristofer Dovstam Ericsson Research
2 outline Luminance (Y) and luma (Y ) Strange things that happen during subsampling How to do subsampling without artifacts Implementation details Applications Bonus? Ericsson Internal Page 2
3 luminance Assume linear (R, G, B) tuple (think linear output of red source, etc ) Luminance is weighted average = + + Linear How bright Y in CEI1931 XYZ color space = 3 3 Ericsson Internal Page 3
4 Luma Non-linear values (R, G, B ): = or = = or = = or = Luma is weighted average = + + Non-linear Not how bright Y in Rec.709 and BT.2020 Y CbCr = 3 3 Ericsson Internal Page 4
5 Preprocessing Ericsson Internal Page 5
6 Preprocessing Ericsson Internal Page 6 inverse gamma a.k.a. inverse Electro Optical Transfer Function (EOTF -1 ). We use SMPTE ST 2084 (PQ)
7 Preprocessing Ericsson Internal Page 7
8 Preprocessing Ericsson Internal Page 8
9 Preprocessing Ericsson Internal Page 9
10 POST PROCESSING Ericsson Internal Page 10
11 POST PROCESSING Ericsson Internal Page 11
12 POST PROCESSING Ericsson Internal Page 12
13 POST PROCESSING Ericsson Internal Page 13
14 POST PROCESSING Ericsson Internal Page 14
15 artifacts original 4:4:4 subsampled to 4:2:0 (no compression!) Ericsson Internal Page 15 Sequence courtesy of Technicolor and the NevEx project
16 Artifacts Spotted by E. Francois in an MPEG contribution Also noted that the effect is biggest for saturated colors Ericsson Internal Page 16
17 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 Ericsson Internal Page 17
18 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 After conversion to Y CbCr: 1= 263, 646, 831 2= 401, 571, 735 Ericsson Internal Page 18
19 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 After conversion to Y CbCr: 1= 263, 646, 831 2= 401, 571, 735 Quite similar Quite different Ericsson Internal Page 19
20 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 After conversion to Y CbCr: 1= 263, 646, 831 2= 401, 571, 735 After averaging = 332, 608.5, 783 Ericsson Internal Page 20
21 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 After conversion to Y CbCr: 1= 263, 646, 831 2= 401, 571, 735 After averaging = 332, 608.5, 783 After converting back to RGB: = 1001, 0.48, Ericsson Internal Page 21
22 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 After conversion to Y CbCr: 1= 263, 646, 831 2= 401, 571, 735 After averaging = 332, 608.5, 783 After converting back to RGB: = 1001, 0.48, Quite similar Ericsson Internal Page 22
23 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 After conversion to Y CbCr: 1= 263, 646, 831 2= 401, 571, 735 After averaging = 332, 608.5, 783 After converting back to RGB: = 1001, 0.48, Quite similar Ericsson Internal Page 23
24 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 After conversion to Y CbCr: 1= 263, 646, 831 2= 401, 571, 735 After averaging = 332, 608.5, 783 After converting back to RGB: = 1001, 0.48, Conclusion: Just averaging is not a problem. Quite similar Ericsson Internal Page 24
25 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 After conversion to Y CbCr: 1= 263, 646, 831 2= 401, 571, 735 After averaging = 332, 608.5, 783 Average Cb/Cr, copy Y : 1= 2= Ericsson Internal Page 25
26 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 After conversion to Y CbCr: 1= 263, 646, 831 2= 401, 571, 735 After averaging = 332, 608.5, 783 Average Cb/Cr, copy Y : 1= 332, 608.5, 783 2= 332,608.5,783 Ericsson Internal Page 26
27 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 After conversion to Y CbCr: 1= 263, 646, 831 2= 401, 571, 735 After averaging = 332, 608.5, 783 Average Cb/Cr, copy Y : 1= 2= 332, 608.5, , 608.5, 783 After conversion back to RGB: 1= 484, 0.03, 45 2= 2061, 2.2, 216 Ericsson Internal Page 27
28 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 After conversion to Y CbCr: 1= 263, 646, 831 2= 401, 571, 735 After averaging = 332, 608.5, 783 Average Cb/Cr, copy Y : 1= 2= 332, 608.5, , 608.5, 783 After conversion back to RGB: 1= 484, 0.03, 45 2= 2061, 2.2, 216 Way too dark! Ericsson Internal Page 28
29 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 After conversion to Y CbCr: 1= 263, 646, 831 2= 401, 571, 735 After averaging = 332, 608.5, 783 Average Cb/Cr, copy Y : 1= 2= 332, 608.5, , 608.5, 783 After conversion back to RGB: 1= 484, 0.03, 45 2= 2061, 2.2, 216 Way too bright! Ericsson Internal Page 29
30 What is happening? Two neighboring pixels: 1= 1000, 0, 100 2= 1000, 4, 100 After conversion to Y CbCr: 1= 263, 646, 831 2= 401, 571, 735 After averaging = 332, 608.5, 783 Average Cb/Cr, copy Y : 1= 2= After conversion back to RGB: 1= 484, 0.03, 45 2= 2061, 2.2, 216 Conclusion: 332, 608.5, , 608.5, 783 Subsampling of two components and copying of third component creates trouble. Ericsson Internal Page 30
31 Main idea Y will make pixels too dark or too bright But! We can set Y individually for each pixel. Why not set it so that the pixel is just as bright as the original? This means matching luminance Y Task: Find Y (luma) that gives the right Y (luminance) We call it Luma Adjustment Ericsson Internal Page 31
32 Overview linear preprocessing 4:2:0 post processing linear RGB to XYZ linear luminance RGB to XYZ linear luminance 4:2:0 subsampling not really preserving Ericsson Internal Page 32
33 first approach Y is an integer in [0, 1023] (or [64, 940] in standard range). Try all Y values and see which generates the best luminance Y Very slow... Lucky thing: Luminance Y is monotonously increasing with Y If Y gives something that is too bright, the optimum Y must be smaller than that value Ericsson Internal Page 33
34 luma adjustment linear preprocessing 4:2:0 post processing linear RGB to XYZ linear luminance compare and if too big, lower RGB to XYZ linear luminance if too small, increase Ericsson Internal Page 34
35 binary search 940 We know the value must be between 64 and Ericsson Internal Page 35
36 binary search We know the value must be between 64 and 940 We try the middle value (64+940)/2 = Ericsson Internal Page 36
37 binary search We know the value must be between 64 and 940 We try the middle value (64+940)/2 = 502 If = 502 generates a linear luminance that is too bright, we know the best must be in [64, 502] Ericsson Internal Page 37
38 binary search We know the value must be between 64 and 940 We try the middle value (64+940)/2 = 502 If = 502 generates a linear luminance that is too bright, we know the best must be in [64, 502] If = (64+502)/2 = 283 makes too dark, should be in [283, 502] etc. Ericsson Internal Page 38
39 binary search We know the value must be between 64 and 940 We try the middle value (64+940)/2 = 502 If = 502 generates a linear luminance that is too bright, we know the best must be in [64, 502] If = (64+502)/2 = 283 makes too dark, should be in [283, 502] etc. 10 iterations instead of 1024 Ericsson Internal Page 39
40 Further refinement: Bounds The fact that the EOTF is monotonously increasing can be used to create a lower and upper bound on Y given Y: Upper Lower +max, +,,, +min, +,,, An even tighter upper bound can be used due to the EOTF being convex. Ericsson Internal Page 40
41 Further Refinement: Bounds Ericsson Internal Page 41
42 Further Refinement: Bounds 940 Upper bound Calculate bounds Lower bound Ericsson Internal Page 42
43 Further Refinement: Bounds 940 Upper bound Calculate bounds Continue interval halving from there Lower bound Ericsson Internal Page 43
44 Further Refinement: Bounds 940 Upper bound Calculate bounds Continue interval halving from there Average number of iterations in a test sequence: < 5 iterations 64 Lower bound Ericsson Internal Page 44
45 Further Refinement: Bounds 940 Upper bound Calculate bounds Continue interval halving from there Average number of iterations in a test sequence: < 5 iterations 64 Lower bound Tighter bounds < 2 iterations Ericsson Internal Page 45
46 Further Refinement: Bounds 940 Upper bound Calculate bounds Continue interval halving from there Average number of iterations in a test sequence: < 5 iterations 64 Ericsson Internal Page 46 Lower bound Tighter bounds < 2 iterations Other approches: LUTs, linearizations
47 results We do not have any material that fills out the BT.2020 color container. In the future, we will have such material. Example images are created using Rec.709 container and material that fills out the Rec.709 gamut. Ericsson Internal Page 47
48 examples Reduced artifacts Original 4:4:4 Traditional subsampling (no compression) Luma Adjustment (proposed method) Ericsson Internal Page 48 Sequence courtesy of Technicolor and the NevEx project
49 examples Original 4:4:4 Traditional subsampling (no compression) Luma Adjustment (proposed method) Ericsson Internal Page 49 Sequence courtesy of Technicolor and the NevEx project
50 Compressed examples Original 4:4:4 Traditional Processing (18752 kbps ) Luma Adjustment (17700 kbps) Ericsson Internal Page 50 Sequence courtesy of Technicolor and the NevEx project
51 Compressed examples Original 4:4:4 Ericsson Internal Page 51 Traditional Processing (26568 kbps) Sequence courtesy of Technicolor and the NevEx project Luma Adjustment (26142 kbps)
52 Applications Encoding for HDR10 (HEVC Main 10, ST 2084, Y CbCr) Transmission chain Receiver chain unchanged Luma Adjustment Ericsson Internal Page 52
53 applications Conversion for display using HDMI 2.0a Any HDR decoder Conversion to Y CbCr 4:2:0 HDMI 2.0a 4k 60 Hz Y CbCr 4:2:0 Traditional RGB to Y CbCr conversion here......gives bad artifacts here. Ericsson Internal Page 53
54 applications Conversion for display using HDMI 2.0a Any HDR decoder Conversion to Y CbCr 4:2:0 HDMI 2.0a 4k 60 Hz Y CbCr 4:2:0 Luma Adjustment here......gives good picture here. Ericsson Internal Page 54
55 Other EOTFs We tried this for SMPTE ST What about other transfer functions? Hybrid Log Gamma (ARIB B67) Ericsson Internal Page 55
56 Hybrid log gamma Original 4:4:4 Traditional subsampling (no compression) Luma Adjustment (proposed method) Ericsson Internal Page 56 Sequence courtesy of Technicolor and the NevEx project
57 Other EOTFs We tried this for SMPTE ST What about other transfer functions? Hybrid Log Gamma What about Standard Dynamic Range (SDR) (Rec 1886)? Ericsson Internal Page 57
58 SDR (1886) original 4:4:4 traditional processing Luma Adjustment Ericsson Internal Page 58
59 SDR (1886) original 4:4:4 traditional processing Luma Adjustment Ericsson Internal Page 59 Sequence courtesy of Technicolor and the NevEx project
60 conclusion Due to non-linearity of transfer function, artifacts appear during subsampling from 4:4:4 to 4:2:0 Worse for HDR since transfer function is more non-linear It is possible to adjust Y to match luminance in each pixel Since Y is monotonous in Y, binary search can be used Mathematical bounds further lower number of iterations Applications are preprocessing for compression and conversion to HDMI 2.0a. Some noise and blurring in 4:2:0 SDR data can be due to the processing from 4:4:4 to 4:2:0. MPEG anchors Ericsson Internal Page 60
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62 HDMI 2.0 and hdmi 2.0a For 4K resolutions at Hz, 4:2:0 is necessary. HMDI 2.0a specifies HDR through pointing to the CEA specification. Ericsson Internal Page 62
63 Y easier to code Y traditional processing Y after Luma Adjustment Ericsson Internal Page 63
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