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首页高效H.263到H.264/AVC视频转码技术与增强速率控制
高效H.263到H.264/AVC视频转码技术与增强速率控制
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更新于2024-08-02
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"这篇论文探讨了从H.263视频到H.264/AVC标准的有效转码方法,利用新编码特性减少计算复杂性,同时保持可接受的视频质量。研究主要集中在语法转码和2:1缩小转码两方面,提出包括快速内预测模式选择、运动矢量重估计和交错模式选择以及H.264/AVC转码增强速率控制在内的三个组件。实验结果表明,所提方法能将总编码时间减少6倍,峰值信噪比(PSNR)损失约为0.35dB。" 在视频编码领域,H.264/AVC标准的出现带来了显著的编码效率和灵活性提升,预期将逐步取代H.263和MPEG-1/2/4等早期标准。该文旨在研究并提出从H.263到H.264/AVC的高效转码策略,以应对转码过程中的计算挑战。其中,快速内预测模式选择通过基于整数变换系数获取的粗略边缘信息来实现,显著加速了处理过程;运动矢量重估计则采用了向量中值滤波技术,改进了原有运动估计的精度;而增强的速率控制利用预编码视频的侧信息,旨在优化视频质量。 论文中提到的方法首先关注了H.264/AVC编码中最耗时的部分,即内预测和运动估计,通过优化这些环节来提升转码速度。然后,通过改进的速率控制,利用预编码的视频信息调整编码参数,以在节省时间的同时,尽可能地减小质量损失。实验证明,这些技术组合应用后,不仅显著减少了转码所需的时间,而且在视频质量上的退化程度非常有限,平均只导致PSNR下降0.35dB,这在实际应用中通常是可以接受的。 这篇研究为H.263视频到H.264/AVC的转码提供了创新的解决方案,平衡了编码效率与视频质量的关系,对于视频处理和流媒体服务等领域具有重要的实践价值。
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V.-A. Nguyen and Y.-P. Tan 3
Input
video
Intraframe
prediction
Motion
compensation
Motion
estimation
Memory
Deblocking
filter
Inverse
transform
Inverse
quantization
Transform Quantization
Quantized
coefficients
Entropy
coding
Intra/inter
+
+
Motion information
Figure 1: Block diagram of a typical H.264/AVC encoder.
Mode 1 (16 × 16) Mode 2 (16 × 8) Mode 3 (8 × 16) Mode 8 × 8
Mode 4
(8
× 8)
Mode 5
(8 × 4)
Mode 6
(4 × 8)
Mode 7
(4 × 4)
Figure 2: Possible modes for motion-compensated prediction in
H.264/AVC.
B
c
(n
j
), the available channel bandwidth u(n
j
), and the frame
rate F
r
as follows:
T
buf
=
u
n
j
F
r
+ γ
TBL
n
j
−
B
c
n
j
,(2)
where γ is a constant and its typical value is 0.75. In addition,
the remaining bits are equally allocated to all not-yet-coded
frames and the number of bits allocated for each frame is
given by
T
r
=
R
r
N
r
,(3)
where R
r
is the number of remaining bits and N
r
is the to-
tal number of not-yet-coded frames. Then, the target bit is a
weighted combination of T
r
and T
buf
,
T
= β × T
r
+(1− β) × T
buf
,(4)
where β is a weighting factor.
A quadratic RD model is used to calculate the corre-
sponding quantization parameter (QP), which is then used
for the RD optimization for each macroblock in the cur-
rent frame. Note that the RD model requires the mean-of-
absolute difference (MAD) of the residue error to estimate
the QP, which is only available after RD optimized process,
thus resulting in a chicken-and-egg problem.
To solve this dilemma, the MAD in the RD model is pre-
dicted by a linear model using the actual MAD of the pre-
vious frames (refer to [12] for details). However, the linear
model assumes the frame complexity varies gradually. If a
scene change occurs, the prediction based on the informa-
tion collected from the previous frames may not be accu-
rate, and in turn it may fail to obtain a suitable QP. Con-
sequently, the number of coding bits for the current frame
may not meet the target allocation bits, resulting in quality
degradation.
In addition, it should be noted that the first I and P
frames in the current group of pictures (GOP) are coded by
using the QP given at the GOP layer, in which the starting
QP of the first GOP is predefined and the starting QPs of
other GOPs are computed based on the QPs of the previ-
ous GOP. Thus, an inappropriately predefined starting QP
can affect the actual achievable bitrate and video quality. Too
small a starting QP would allocate more bits to the first few
frames; hence there would not be enough bits for coding
other frames to closely meet the target bitrate and inconsis-
tent video quality would result. On the other hand, too large
a starting QP would result in a low quality for the first ref-
erence frame, which in turn affects the quality of the subse-
quent frames.
In summary, the advanced coding features in H.264/AVC
can provide a better coding efficiency at the cost of in-
creasing complexity. As many potential applications of video
transcoding require the v ideo to be transcoded in real time
or as fast as possible (e.g., video streaming over heteroge-
nous networks), it is therefore necessary to minimize the
complexity of video transcoding without sacrificing much
its coding efficiency. In this paper, we focus on the most
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