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



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CE2.3: SAO


Test#

Description

Document#

AhG13

Coding gain of SAO in VTM

JVET-K0013

2.3.1.a

SAO with EO sign constraints removal

JVET-K0233

2.3.1.b

SAO with EO sign constraints removal and more EO patterns

JVET-K0233

2.3.2.a

SAO Merge with 128×128 unit

JVET-K0324

2.3.2.b

SAO Merge with 128×128 or 256×256 unit

JVET-K0324

2.3.2.c

SAO Merge with 64×64 or 128×128 unit

JVET-K0324

2.3.3.a

SAO Palette results and discussion: Test a (fixed block size, as LCU)

JVET-K0192

2.3.3.b

SAO Palette results and discussion: Test b (adaptive block size)

JVET-K0192

2.3.4.a

SAO Modification: EO (modified threshold)

JVET-K0176

2.3.4.b

SAO Modification: BO offset quantization

JVET-K0176

2.3.4.c

SAO Modification: BO offset band (fixed instead of being signalled)

JVET-K0176

2.3.4.d

SAO Modification: shifted region

JVET-K0176

2.3.4.e

SAO Modification: EO and BO offset range

JVET-K0176

2.3.4.f

SAO Modification: All

JVET-K0176

2.3.4.g

SAO Modification: a + b + d

JVET-K0176

2.3.5

CTU adaptive sample adaptive offset (modified band position and range)

JVET-K0153

Results vs. VTM:

 

AI

RA

LDB

Test#

Y

U

V

EncT

DecT

Y

U

V

EncT

DecT

Y

U

V

EncT

DecT

AhG13














































2.3.1.a

0.00%

0.03%

0.03%

100%

100%

-0.04%

-0.10%

-0.07%

100%

101%

-0.17%

0.06%

-0.04%

100%

99%

2.3.1.b

-0.04%

-0.06%

-0.07%

101%

100%

-0.13%

-0.21%

-0.11%

100%

100%

-0.15%

-0.32%

-0.21%

100%

100%

2.3.2.a

-0.12%

-0.86%

-0.92%

99%

104%

-0.25%

-2.00%

-1.82%

101%

109%

-0.28%

-2.99%

-3.68%

101%

104%

2.3.2.b

-0.12%

-0.84%

-0.91%

99%

103%

-0.26%

-2.02%

-1.85%

100%

107%

-0.25%

-2.80%

-3.58%

100%

105%

2.3.2.c

-0.11%

-0.97%

-1.12%

93%

97%

-0.30%

-2.10%

-2.10%

100%

100%

-0.43%

-3.28%

-4.30%

96%

96%

2.3.3.a

-0.17%

-0.43%

-0.43%

100%

92%

-0.29%

-0.90%

-0.72%

100%

87%

-0.37%

-1.60%

-1.87%

96%

92%

2.3.3.b

-0.17%

-0.33%

-0.41%

102%

93%

-0.38%

-1.16%

-1.03%

97%

100%

-0.52%

-2.07%

-2.75%

98%

111%

2.3.4.a

-0.08%

-0.15%

-0.18%

99%

96%

-0.12%

0.08%

0.14%

97%

94%

0.03%

0.51%

0.66%

98%

95%

2.3.4.b

-0.04%

0.39%

0.51%

99%

97%

-0.04%

0.43%

0.51%

97%

97%

-0.05%

0.06%

0.38%

100%

103%

2.3.4.c

-0.06%

0.86%

1.17%

100%

98%

0.13%

0.97%

1.29%

98%

101%

0.49%

1.59%

2.82%

100%

101%

2.3.4.d

0.00%

-0.13%

-0.15%

99%

96%

-0.01%

-0.15%

-0.14%

97%

94%

0.02%

-0.20%

-0.49%

98%

95%

2.3.4.e

0.00%

0.00%

0.01%

100%

100%

-0.01%

0.00%

0.03%

98%

96%

0.02%

0.03%

-0.06%

101%

101%

2.3.4.f

-0.15%

0.63%

0.90%

100%

96%

-0.02%

1.06%

1.44%

97%

94%

0.46%

2.30%

3.71%

98%

95%

2.3.4.g

-0.13%

0.17%

0.23%

100%

98%

-0.17%

0.46%

0.62%

98%

98%

-0.06%

0.58%

1.14%

99%

97%

2.3.5

0.06%

-0.75%

-0.84%

100%

101%

-0.06%

-0.92%

-0.94%

100%

100%

-0.15%

-1.52%

-2.03%

100%

101%

Results vs. BMS:



 

AI

RA

LDB

Test#

Y

U

V

EncT

DecT

Y

U

V

EncT

DecT

Y

U

V

EncT

DecT

AhG13














































2.3.1.a

0.00%

-0.01%

-0.07%

100%

100%

0.02%

-0.08%

-0.06%

100%

100%

-0.07%

-0.07%

-0.04%

100%

100%

2.3.1.b

-0.03%

-0.03%

-0.12%

100%

100%

-0.03%

-0.24%

-0.17%

100%

100%

-0.14%

-0.57%

-0.30%

101%

100%

2.3.2.a

-0.09%

-0.74%

-0.69%

99%

102%

-0.15%

-2.34%

-2.19%

101%

105%

-0.37%

-3.37%

-3.99%

100%

103%

2.3.2.b

-0.10%

-0.70%

-0.67%

98%

101%

-0.17%

-2.36%

-2.20%

99%

104%

-0.42%

-3.53%

-4.27%

99%

103%

2.3.2.c

-0.08%

-0.87%

-0.92%

98%

98%

-0.18%

-2.43%

-2.40%

101%

103%

-0.42%

-3.77%

-4.60%

99%

101%

2.3.3.a

-0.15%

-0.13%

0.00%

99%

94%

-0.18%

-1.10%

-0.87%

101%

92%

-0.47%

-1.99%

-2.43%

101%

97%

2.3.3.b

-0.14%

-0.02%

0.07%

101%

96%

-0.24%

-1.38%

-1.23%

100%

95%

-0.56%

-2.60%

-3.15%

105%

97%

2.3.4.a

0.00%

0.11%

0.02%

100%

98%

0.01%

0.10%

0.05%

98%

98%

-0.05%

0.21%

0.71%

101%

97%

2.3.4.b

-0.04%

0.63%

0.70%

101%

105%

0.02%

0.50%

0.59%

100%

101%

-0.11%

0.03%

0.30%

101%

103%

2.3.4.c

-0.07%

1.22%

1.43%

102%

106%

0.08%

0.83%

1.05%

100%

102%

0.09%

1.26%

2.18%

101%

105%

2.3.4.d

0.00%

-0.13%

-0.13%

100%

98%

0.01%

-0.17%

-0.13%

98%

98%

-0.07%

-0.41%

-0.19%

99%

97%

2.3.4.e

0.00%

0.00%

0.00%

100%

100%

0.02%

-0.01%

0.01%

99%

99%

-0.09%

0.01%

0.07%

100%

100%

2.3.4.f

-0.08%

1.37%

1.49%

101%

100%

0.04%

0.99%

1.14%

97%

99%

0.09%

1.80%

3.39%

98%

97%

2.3.4.g
















-0.01%

0.59%

0.67%

98%

99%
















2.3.5

0.07%

-0.90%

-1.02%

100%

102%

0.02%

-0.88%

-0.96%

100%

100%

-0.13%

-1.62%

-1.99%

100%

101%

2.3.2.x and 2.3.3.x have picture-level optimization and give gain up to 0.4% in RA compared to VTM, and It was remarked that the same effect could be achieved by a smarter encoder e.g. performing lookahead

The methods that do not use picture level optimization provide small gain (<0.2% for RA compared to VTM)

Compared to BMS, the methods without picture level opt. do not provide gain any more in RA, and the gain of the picture-level methods reduces to <0.25%

Generally, the gain in LDB is slightly higher (around 0.5% vs VTM and BMS for picture level opt.), where however this would introduce one frame delay at the encoder.

The results do not suggest any action at this moment. a) No evidence is currently available that there is subjective improvement; b) when combined with ALF, most gain is lost; c) more smart encoders likely could get similar gain by picture-level optimization (at the expense of additional encoding delay) without syntax change.


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