Joint Video Experts Team (jvet) of itu-t sg 6 wp and iso/iec jtc 1/sc 29/wg 11

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CE2.4 Adaptive Loop Filters


Test#

Description

Document#

AhG13

Coding gain of ALF in BMS

JVET-K0013

2.4.1.1.a

Subsampled sum-modified-Laplacian with 4×4 level classification

JVET-K0164

2.4.1.1.b

Subsampled sum-modified-Laplacian with 2×2 level classification

JVET-K0164

2.4.1.2

Adaptive Loop Filter Simplification

JVET-K0327

2.4.1.3

ALF with Multiplication Replaced by Bit-Shifting

JVET-K0215

2.4.1.4.a

luma 7×7, classifier 2×2, chroma 5×5

JVET-K0371

2.4.1.4.b

luma 7×7, classifier 2×2, chroma 7×7

JVET-K0371

2.4.1.4.c

luma 7×7, classifier 4×4, chroma 5×5

JVET-K0371

2.4.1.4.d

luma 7×7, classifier 4×4, chroma 7×7

JVET-K0371

2.4.1.4.e

luma 9×9, classifier 2×2, chroma 5×5

JVET-K0371

2.4.1.4.f

luma 9×9, classifier 2×2, chroma 7×7

JVET-K0371

2.4.1.4.g

luma 9×9, classifier 4×4, chroma 5×5

JVET-K0371

2.4.1.4.h

luma 9×9, classifier 4×4, chroma 7×7

JVET-K0371

2.4.1.4.i

deblocking TC offset setting (-6 for AI, and TC offset -2 for RA and LB)

JVET-K0371

2.4.2.1.a

Multiple-feature based adaptive loop filter (MCALF)

with 4 classifiers (whole proposal)

JVET-K0285

2.4.2.1.b

MCALF with two classifiers (GALF classification) and

(sample-based classification, like BO)

JVET-K0285

2.4.2.1.c

MCALF with two classifiers and

(ranking with sample-based classification, 3×3 pattern)

(counting number of samples that are larger or smaller)

JVET-K0285

2.4.2.1.d

MCALF with two classifiers and

(ranking with local variation-based, cross-sign pattern)

(checking difference in neighbourhood)

JVET-K0285

2.4.2.2.a

CTB-based ALF with slice filter sets, 3 classification methods (whole proposal)

( IntensitySA (like BO), SimilaritySA(5x5 diamond),

and GeometricBA (GALF classification))

JVET-K0235

2.4.2.2.b

CTB-based ALF with slice filter sets, only IntensitySA and GeometricBA

JVET-K0235

2.4.2.2.c

CTB-based ALF with slice filter sets, only GeometricBA

JVET-K0235

2.4.2.2.d

CTB-based ALF with slice filter sets, only IntensitySA

JVET-K0235

2.4.2.2.e

CTB-based ALF with slice filter sets, single filter is selected at CTB


(including merge with neighbour signalling)


JVET-K0235

2.4.2.3

Unified Adaptive Loop Filter for Luma and Chroma

JVET-K0132

2.4.2.4

Modified ALF classification: horizontal and vertical gradients

are calculated using Sobel filter

JVET-K0151

Properties of Methods:




Test#

Block classification granularity





# Samples for 1-D Laplacian value calculations

(worst case)



Line buffer size

Filter supports

FF

Filter

Max. Num. of filters per slice to be stored

Classification method selection

Filter decision require whole slice data?

BMS

2×2 (L), N/A (C)




# samples in the whole slice (M)

8 (L), 4 (C)

9×9/7×7/5×5 diamond (L);

5×5 diamond (C)



Y

×

25 (L)

1 (C)


N/A

Y

2.4.1.1.a

4×4 (L), N/A (C)




M/2

(one dimension

subsample by 2)


8 (L), 4 (C)

Same as BMS

Y

×

25 (L)

1 (C)


N/A

Y

2.4.1.1.b

2×2 (L), N/A (C)




M/2

(one dimension

subsample by 2)


8 (L), 4 (C)

Same as BMS

Y

×

25 (L)

1 (C)


N/A

Y

2.4.1.2

2×2 (L), N/A (C)




M/4 for highest temporal layer; and M for others

(two dimensions subsampled by 2)




8 (L), 4 (C)

Same as BMS

Y

×

25 (L)

1 (C)


N/A

Y

2.4.1.3

2×2 (L), N/A (C)




M

8 (L), 4 (C)

Same as BMS

N

× and <<

25 (L)

1 (C)


N/A

Y

2.4.1.4.a

2×2 (L), N/A (C)




M

6(L), 4(C)

7×7 (L), 5×5 (C)

Y

×

25 (L)

1 (C)


N/A

Y

2.4.1.4.b

2×2 (L), N/A (C)




M

6(L), 6(C)

7×7 (L), 7×7 (C)

Y

×

25 (L)

1 (C)


N/A

Y

2.4.1.4.c

4×4 (L), N/A (C)




M

6(L), 4(C)

7×7 (L), 5×5 (C)

Y

×

25 (L)

1 (C)


N/A

Y

2.4.1.4.d

4×4 (L), N/A (C)




M

6(L), 6(C)

7×7 (L), 7×7 (C)

Y

×

25 (L)

1 (C)


N/A

Y

2.4.1.4.e

2×2 (L), N/A (C)




M

8(L), 4(C)

9×9 (L), 5×5 (C)

Y

×

25 (L)

1 (C)


N/A

Y

2.4.1.4.f

2×2 (L), N/A (C)




M

8(L), 6(C)

9×9 (L), 7×7 (C)

Y

×

25 (L)

1 (C)


N/A

Y

2.4.1.4.g

4×4 (L), N/A (C)




M

8(L), 4(C)

9×9 (L), 5×5 (C)

Y

×

25 (L)

1 (C)


N/A

Y

2.4.1.4.h

4×4 (L), N/A (C)




M

8(L), 6(C)

9×9 (L), 7×7 (C)

Y

×

25 (L)

1 (C)


N/A

Y

2.4.1.4.i




Same as in BMS

25 (L)

1 (C)


N/A

Y

2.4.2.1

2×2, 1×1 (L),

N/A (C)





M

8 (L), 4 (C)

Same as BMS

Y

×

16 (L)

1 (C)


Slice-level

Y

2.4.2.2.a

2×2, 1×1 (L),

N/A (C)





M

8(L), 8(C)

9×9 Cross +3×3Square with half or symmetry

N

×

16 (L)

2 (C)


Slice-level

Y

2.4.2.2.b

2×2, 1×1 (L),

N/A (C)





M

8(L), 8(C)

9×9 Cross +3×3Square with half or symmetry

N

×

16 (L)

2 (C)


Slice-level

Y

2.4.2.2.c

2×2 (L)

N/A (C)





M

8(L), 8(C)

9×9 Cross +3×3Square with half or symmetry

N

×

16 (L)

2 (C)


Slice-level

Y

2.4.2.2.d

1×1 (L),

N/A (C)





M

8(L), 8(C)

9×9 Cross +3×3Square with half or symmetry

N

×

16 (L)

2(C)


Slice-level

Y

2.4.2.2.e

N/A (L),

N/A (C)





0

8(L), 8(C)

9×9 Cross +3×3Square with half or symmetry

N

×

16L + 2C + (# of CTUs in one CTU row) * 3 (L)

2 (C)


N/A

Y

2.4.2.3

2×2 (L),

1×1 (C, inherited from luma)




M

8 (L), 4 (C)

9×9/7×7/5×5 diamond (L);

5×5 diamond (C)



Y

×

25 (L)

1 (C)


N/A

Y

2.4.2.4




Same as BMS (except gradient calculation is replaced by Sobel filter)

Results vs. VTM:

 

AI

RA

LDB

Test#

Y

U

V

EncT

DecT

Y

U

V

EncT

DecT

Y

U

V

EncT

DecT

AhG13

-3.30%

-3.55%

-4.06%

101%

155%

-5.34%

-2.21%

-1.66%

108%

184%

-

-

-

-

-

2.4.1.1.a

-3.11%

-3.55%

-4.06%

110%

205%

-5.18%

-2.26%

-1.65%

113%

268%

-4.41%

-1.80%

-1.85%

119%

216%

2.4.1.1.b

-3.19%

-3.55%

-4.06%

111%

209%

-5.25%

-2.19%

-1.64%

114%

273%

-4.45%

-1.81%

-1.94%

120%

219%

2.4.1.2

-3.30%

-3.55%

-4.06%

102%

170%

-5.26%

-2.23%

-1.67%

109%

200%

-4.55%

-1.80%

-1.95%

114%

170%

2.4.1.3

-3.04%

-3.44%

-3.95%

102%

149%

-5.15%

-2.19%

-1.56%

107%

176%

-4.52%

-1.79%

-2.16%

112%

154%

2.4.1.4.a*

-3.10%

-3.56%

-4.07%

101%

124%

-5.10%

-2.29%

-1.69%

105%

135%

-4.32%

-1.79%

-1.77%

106%

119%

2.4.1.4.b*

-3.09%

-4.20%

-4.73%

101%

123%

-5.11%

-3.16%

-2.66%

104%

134%

-4.31%

-2.63%

-2.72%

107%

122%

2.4.1.4.c*

-2.99%

-3.56%

-4.07%

101%

114%

-4.99%

-2.24%

-1.66%

104%

122%

-4.19%

-1.64%

-1.85%

107%

114%

2.4.1.4.d*

-2.98%

-4.20%

-4.73%

102%

114%

-4.98%

-3.14%

-2.66%

104%

122%

-4.23%

-2.44%

-2.57%

107%

116%

2.4.1.4.e*

-3.28%

-3.54%

-4.05%

103%

130%

-5.33%

-2.21%

-1.63%

108%

143%

-4.56%

-1.81%

-2.01%

110%

125%

2.4.1.4.f*

-3.27%

-4.19%

-4.71%

102%

129%

-5.35%

-3.11%

-2.57%

107%

142%

-4.63%

-2.61%

-2.88%

111%

128%

2.4.1.4.g*

-3.17%

-3.54%

-4.05%

102%

119%

-5.21%

-2.20%

-1.66%

107%

129%

-4.46%

-1.83%

-2.01%

110%

117%

2.4.1.4.h*

-3.16%

-4.19%

-4.71%

103%

121%

-5.23%

-3.15%

-2.63%

108%

129%

-4.49%

-2.59%

-2.72%

111%

116%

2.4.1.4.i

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

2.4.2.1.a

-3.32%

-3.55%

-4.06%

103%

162%

-5.61%

-2.22%

-1.66%

111%

197%

-5.19%

-1.74%

-2.04%

117%

167%

2.4.2.1.b

-3.31%

-3.55%

-4.06%

102%

165%

-5.53%

-2.21%

-1.67%

109%

200%

-4.97%

-1.96%

-2.13%

115%

167%

2.4.2.1.c

-3.31%

-3.55%

-4.06%

102%

164%

-5.60%

-2.21%

-1.64%

109%

197%

-5.14%

-1.99%

-1.94%

113%

165%

2.4.2.1.d

-3.30%

-3.55%

-4.06%

102%

166%

-5.53%

-2.24%

-1.66%

110%

201%

-4.90%

-1.67%

-2.04%

115%

169%

2.4.2.2.a

-2.70%

-4.94%

-5.69%

102%

133%

-4.85%

-5.25%

-5.08%

101%

145%

-4.60%

-7.42%

-7.19%

102%

137%

2.4.2.2.b

-2.69%

-4.94%

-5.69%

101%

132%

-4.82%

-5.24%

-5.09%

101%

144%

-4.52%

-7.51%

-6.97%

101%

135%

2.4.2.2.c

-2.66%

-4.94%

-5.69%

101%

134%

-4.66%

-5.26%

-5.10%

100%

148%

-4.19%

-7.48%

-6.97%

101%

136%

2.4.2.2.d

-2.07%

-4.96%

-5.71%

101%

117%

-3.72%

-5.12%

-4.95%

100%

123%

-3.38%

-7.30%

-6.96%

101%

118%

2.4.2.2.e

-1.89%

-4.97%

-5.71%

101%

114%

-3.41%

-5.16%

-4.96%

100%

118%

-2.94%

-7.33%

-6.95%

101%

113%

2.4.2.3

-3.24%

-4.79%

-5.47%

105%

144%

-5.31%

-6.69%

-5.38%

111%

173%

-4.62%

-8.68%

-7.52%

116%

154%

2.4.2.4**

-3.25%

-3.51%

-4.02%

109%

268%

-5.34%

-2.21%

-1.63%

110%**

370%

-4.68%

-1.55%

-2.04%

124%

275%


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