Temporal domain rate distortion optimization based on video content characteristic and QP-λcorrection

Patent metadata

Field	Value
Publication number	US-11418795-B2
Application number	US-202117460360-A
Country	US
Kind code	B2
Filing date	Aug 30, 2021
Priority date	Aug 5, 2020
Publication date	Aug 16, 2022
Grant date	Aug 16, 2022

Abstract

A temporal domain rate distortion optimization based on video content characteristic and QP-λ correction provides the temporal domain rate distortion optimization based on the video content characteristic and the QP-λ correction for a new generation encoder AV1, wherein according to a previous temporal domain dependency relationship under an HEVC-RA coding structure, a feature of the new generation encoder AV1 and a video sequence feature, an aggregation distortion of a current coding unit and an affected future coding unit is estimated and ta propagation factor of the current coding unit in a temporal domain distortion propagation model is calculated by constructing a temporal domain distortion propagation chain, wherein a Lagrange multiplier is adjusted through a more accurate propagation factor to realize a temporal domain dependency rate distortion optimization, and a relationship of QP-λ is re-corrected and an I frame is adjusted to achieve a better coding effect.

First claim

What is claimed is:

1. A temporal domain rate distortion optimization method based on a video content characteristic and QP-λ correction, comprising the following steps:
S1: establishing a temporal domain propagation chain according to a temporal domain dependency relationship in an AV1 default coding structure, finding a matching block affected by each original coding block through a forward motion search, and recording a corresponding original motion compensation error and a corresponding motion vector;
S2: defining a Lagrange multiplier as λ new and a quantification step size as Qstep, then counting a Lagrange multiplier λ of a different sequence of a different quantification parameter (QP) and a corresponding quantification step size Qstep according to a built-in correspondence list of the QP and the quantification step size Qstep of an encoder, and constructing a relationship model between the Lagrange multiplier λ new and the quantification step size Qstep as follows:

λ
new

=

3.667
*

Qstep
2

-

5.198
⁢
e

-

07
*
Qstep

-
0.664

;

{

λ
org

=

1.1
*

λ
org

,



λ
org

-

λ
new



>

100
⁢

⁢
or
⁢

⁢



λ
org

-

λ
new



<
0.05

⁢

λ
org

=

0.95
*

λ
org

,



λ
org

-

λ
new



<=
3

;

wherein λ org is a Lagrange multiplier in the encoder;
classifying an original video sequence, calculating a sum of absolute values of difference values of subsequent 10, 20, 30 . . . frames relative to an initial first frame by a frame difference method, evaluating an average value of a pixel grade of a cumulative sum as E, and adjusting different QPs, λ adjustment ranges and corresponding α and I frame QP for an obtained result according to a threshold:

SAD
i

=

∑



p
0

-

p

10
*
i



;

E
=

∑

i
=
1

F
/
10

⁢

⁢

SAD
i

W
*
H
*
F
⁢

/

⁢
10

;

Assignees

• Univ Electronic Sci & Tech China

Inventors

• Zhu Ce
• Qin Han
• Wang Yonghua
• Liu Yipeng
• Liu Kai

Classifications

• H04N19/147 Primary

  according to rate distortion criteria (rate-distortion as a criterion for motion estimation H04N19/567) · CPC title

• H04N19/149

  by estimating the code amount by means of a model, e.g. mathematical model or statistical model · CPC title

• H04N19/176

  the region being a block, e.g. a macroblock · CPC title

• H04N19/19

  using optimisation based on Lagrange multipliers · CPC title

• H04N19/124 Primary

  Quantisation · CPC title