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英语翻译~ 急求大神帮忙翻译下这段话~~ 要人工翻译~~~ 谢谢啦 20
Fig.16.PSNRYversusbitratecurve.InFig.16,theanchormeansthatallviewsareencodedseparatel...
Fig. 16. PSNR Y versus bit rate curve.
In Fig. 16, the anchor means that all views are encoded separately
using H.264/AVC with specified parameters [23]. All
PSNR curves represent the average values over all views. Except
for the anchor coding result, it is not possible to compute
the exact bit rate for each view because all data are merged into
one bit stream. We have calculated the total bit rate per LDI
frame and divided it by the number of views because each LDI
frame contains data for all viewpoints hierarchically. Since the
rate control mechanism is not implemented for the LDI frames,
we have manually allocated the total bit rate to each component
of the LDI frame. Its four components have been encoded by the
proposed methods.We have assigned approximately 70% of the
total bit rate to NOL, 20% to the color and depth components,
and 10% to the residual data. From our experimental results,
we observe that the proposed method has benefits in terms of
coding efficiency because it shows a better PSNR curve than the
anchor and a few others dealing with the color component only,
even though it contains all the encoded bits for depth, NOL, and
residual as well as color data.
There are several problems to be considered in future experiments.
First, the relationship between the NOL and the quality
of reconstructed views should be analyzed carefully. Second,
shape adaptive transforms, such as a shape-adaptive discrete cosine/
wavelet transform could be used to encode LDI data because
H.264/AVC supports only the 4 4 integer transform. Finally,
temporal prediction schemes between constructed LDI
frames could be investigated using more test sequences with
depth information. Remaining issues of the LDI-based approach
are how to select the proper back layer pixels to fill out the current
pixel location, how to dynamically allocate total bits to each
component, e.g., color, depth, NOL, and residual, and how to
compare the performance of depth coding. Since the LDI frame
contains the depth information and the PSNR value may not be
the best measure for evaluating the depth coding performance,
we need to develop proper comparison metrics considering view
generation results using the depth information. 展开
In Fig. 16, the anchor means that all views are encoded separately
using H.264/AVC with specified parameters [23]. All
PSNR curves represent the average values over all views. Except
for the anchor coding result, it is not possible to compute
the exact bit rate for each view because all data are merged into
one bit stream. We have calculated the total bit rate per LDI
frame and divided it by the number of views because each LDI
frame contains data for all viewpoints hierarchically. Since the
rate control mechanism is not implemented for the LDI frames,
we have manually allocated the total bit rate to each component
of the LDI frame. Its four components have been encoded by the
proposed methods.We have assigned approximately 70% of the
total bit rate to NOL, 20% to the color and depth components,
and 10% to the residual data. From our experimental results,
we observe that the proposed method has benefits in terms of
coding efficiency because it shows a better PSNR curve than the
anchor and a few others dealing with the color component only,
even though it contains all the encoded bits for depth, NOL, and
residual as well as color data.
There are several problems to be considered in future experiments.
First, the relationship between the NOL and the quality
of reconstructed views should be analyzed carefully. Second,
shape adaptive transforms, such as a shape-adaptive discrete cosine/
wavelet transform could be used to encode LDI data because
H.264/AVC supports only the 4 4 integer transform. Finally,
temporal prediction schemes between constructed LDI
frames could be investigated using more test sequences with
depth information. Remaining issues of the LDI-based approach
are how to select the proper back layer pixels to fill out the current
pixel location, how to dynamically allocate total bits to each
component, e.g., color, depth, NOL, and residual, and how to
compare the performance of depth coding. Since the LDI frame
contains the depth information and the PSNR value may not be
the best measure for evaluating the depth coding performance,
we need to develop proper comparison metrics considering view
generation results using the depth information. 展开
展开全部
Fig. 16. PSNR Y versus bit rate curve.
图16。信噪比是与比特率曲线。
In Fig. 16, the anchor means that all views are encoded separately
在图16中,锚手段,所有意见分别编码
using H.264/AVC with specified parameters [23]. All
使用h.264/avc指定的参数[ 23 ]。所有的
PSNR curves represent the average values over all views. Except
峰值信噪比曲线代表的平均值的所有意见。除
for the anchor coding result, it is not possible to compute
为锚的编码结果,这是不可能计算
the exact bit rate for each view because all data are merged into
确切的比特率为每个视图,所有的数据合并成
one bit stream. We have calculated the total bit rate per LDI
一个位流。我们计算的总比特率低密度脂蛋白
frame and divided it by the number of views because each LDI
框架和除以一些看法,因为每个项目
frame contains data for all viewpoints hierarchically. Since the
框架包含的所有数据,观点层次。自
rate control mechanism is not implemented for the LDI frames,
速率控制机制不实施的项目框架,
we have manually allocated the total bit rate to each component
我们有手动分配的总比特率为每个组件
of the LDI frame. Its four components have been encoded by the
框架的低密度脂蛋白。它的四个组成部分已编码的
proposed methods.We have assigned approximately 70% of the
提出的方法。我们已经安排了约70%的
total bit rate to NOL, 20% to the color and depth components,
总比特率第一,20%的颜色和深度成分,
and 10% to the residual data. From our experimental results,
和10%的剩余数据。从我们的实验结果,
we observe that the proposed method has benefits in terms of
我们注意到,拟议的方法方面的利益
coding efficiency because it shows a better PSNR curve than the
编码效率,因为它显示了较好的峰值信噪比曲线比
anchor and a few others dealing with the color component only,
锚和其他一些处理的分量只有,
even though it contains all the encoded bits for depth, NOL, and
虽然它包含了所有的编码比特深度,北环线,和
residual as well as color data.
残余以及颜色数据。
There are several problems to be considered in future experiments.
有几个问题需要考虑在今后的实验。
First, the relationship between the NOL and the quality
首先,之间的关系,则和质量
of reconstructed views should be analyzed carefully. Second,
重建的意见应当仔细分析。二,
shape adaptive transforms, such as a shape-adaptive discrete cosine/
形状自适应变换,如形状自适应离散余弦/
wavelet transform could be used to encode LDI data because
小波变换可以用来编码数据,因为低密度脂蛋白
H.264/AVC supports only the 4 4 integer transform. Finally,
h.264/avc只支持4×4整数变换。最后,
temporal prediction schemes between constructed LDI
时间预测方案建造低密度脂蛋白
frames could be investigated using more test sequences with
框架可以用多个测试序列
depth information. Remaining issues of the LDI-based approach
深度信息。剩下的问题的ldi-based方法
are how to select the proper back layer pixels to fill out the current
如何选择合适的背层像素填写的
pixel location, how to dynamically allocate total bits to each
像素的位置,如何动态分配的总比特每
component, e.g., color, depth, NOL, and residual, and how to
组件,例如,颜色,深度,北环线,和残余,以及如何
compare the performance of depth coding. Since the LDI frame
比较性能深度编码。由于低密度脂蛋白框架
contains the depth information and the PSNR value may not be
包含深度信息和信噪比的价值可能不
the best measure for evaluating the depth coding performance,
最好的措施,评价深度编码的性能,
we need to develop proper comparison metrics considering view
我们需要制定适当的指标考虑观比较
generation results using the depth information.
结果利用深度信息。
图16。信噪比是与比特率曲线。
In Fig. 16, the anchor means that all views are encoded separately
在图16中,锚手段,所有意见分别编码
using H.264/AVC with specified parameters [23]. All
使用h.264/avc指定的参数[ 23 ]。所有的
PSNR curves represent the average values over all views. Except
峰值信噪比曲线代表的平均值的所有意见。除
for the anchor coding result, it is not possible to compute
为锚的编码结果,这是不可能计算
the exact bit rate for each view because all data are merged into
确切的比特率为每个视图,所有的数据合并成
one bit stream. We have calculated the total bit rate per LDI
一个位流。我们计算的总比特率低密度脂蛋白
frame and divided it by the number of views because each LDI
框架和除以一些看法,因为每个项目
frame contains data for all viewpoints hierarchically. Since the
框架包含的所有数据,观点层次。自
rate control mechanism is not implemented for the LDI frames,
速率控制机制不实施的项目框架,
we have manually allocated the total bit rate to each component
我们有手动分配的总比特率为每个组件
of the LDI frame. Its four components have been encoded by the
框架的低密度脂蛋白。它的四个组成部分已编码的
proposed methods.We have assigned approximately 70% of the
提出的方法。我们已经安排了约70%的
total bit rate to NOL, 20% to the color and depth components,
总比特率第一,20%的颜色和深度成分,
and 10% to the residual data. From our experimental results,
和10%的剩余数据。从我们的实验结果,
we observe that the proposed method has benefits in terms of
我们注意到,拟议的方法方面的利益
coding efficiency because it shows a better PSNR curve than the
编码效率,因为它显示了较好的峰值信噪比曲线比
anchor and a few others dealing with the color component only,
锚和其他一些处理的分量只有,
even though it contains all the encoded bits for depth, NOL, and
虽然它包含了所有的编码比特深度,北环线,和
residual as well as color data.
残余以及颜色数据。
There are several problems to be considered in future experiments.
有几个问题需要考虑在今后的实验。
First, the relationship between the NOL and the quality
首先,之间的关系,则和质量
of reconstructed views should be analyzed carefully. Second,
重建的意见应当仔细分析。二,
shape adaptive transforms, such as a shape-adaptive discrete cosine/
形状自适应变换,如形状自适应离散余弦/
wavelet transform could be used to encode LDI data because
小波变换可以用来编码数据,因为低密度脂蛋白
H.264/AVC supports only the 4 4 integer transform. Finally,
h.264/avc只支持4×4整数变换。最后,
temporal prediction schemes between constructed LDI
时间预测方案建造低密度脂蛋白
frames could be investigated using more test sequences with
框架可以用多个测试序列
depth information. Remaining issues of the LDI-based approach
深度信息。剩下的问题的ldi-based方法
are how to select the proper back layer pixels to fill out the current
如何选择合适的背层像素填写的
pixel location, how to dynamically allocate total bits to each
像素的位置,如何动态分配的总比特每
component, e.g., color, depth, NOL, and residual, and how to
组件,例如,颜色,深度,北环线,和残余,以及如何
compare the performance of depth coding. Since the LDI frame
比较性能深度编码。由于低密度脂蛋白框架
contains the depth information and the PSNR value may not be
包含深度信息和信噪比的价值可能不
the best measure for evaluating the depth coding performance,
最好的措施,评价深度编码的性能,
we need to develop proper comparison metrics considering view
我们需要制定适当的指标考虑观比较
generation results using the depth information.
结果利用深度信息。
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图16。信噪比是与比特率曲线。在图16中,锚手段,所有视图编码分别用h.264/avc指定的参数[ 23 ]。所有的信噪比曲线代表的平均值的所有意见。除锚编码结果,这是不可能计算出精确的比特率为每个视图,所有的数据合并成一个位流。我们计算的总比特率低密度脂蛋白和除以一些看法,因为每个项目包含的所有数据,观点层次框架。由于速率控制机制不实施的项目,我们有手动分配的总比特率的每个组件的低密度脂蛋白框架。它的四个组成部分已编码的方法。我们已经安排了大约70%的总比特率第一,20%的颜色和深度成分,和10%的剩余数据。从我们的实验结果,我们观察到,该方法的好处方面的编码效率,因为它显示了较好的峰值信噪比曲线比锚和其他一些处理的颜色组成部分,虽然它包含所有的编码比特深度,北环线,和残余以及颜色数据。有几个问题需要考虑在今后的实验。首先,之间的关系,则和重建质量观应该仔细分析。其次,形状自适应变换,如形状自适应离散余弦和小波变换可以用来编码数据,因为低密度脂蛋白h.264/avc只支持4×4整数变换。最后,时间预测计划建造低密度脂蛋白之间的帧可以进行调查,使用多个测试序列与深度信息。剩下的问题的ldi-based方法是如何选择合适的背层像素填写当前像素位置,如何动态分配的总比特的每一个组成部分,例如,颜色,深度,北环线,和残余,以及如何表现比较深入的编码。由于低密度脂蛋白框架包含深度信息和信噪比的值可能不是最好的措施,评价的深度编码的性能,我们需要制定适当的比较度量考虑视图生成结果使用深度信息。
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图。 16。 PSNR Y与比特率曲线。
另外,在图图16中,锚定装置的所有视图分开进行编码
使用H.264/AVC与指定的参数[23]。所有
PSNR曲线所代表的平均值在各方面的意见。除
锚定编码结果,它是不可能计算
确切的比特率,因为所有的数据都合并到每个视图
一个比特流。我们已计算出的总比特率每LDI
帧,并且把它的视图数,因为每个LDI
帧包含所有观点的数据分层。由于
没有实现的LDI帧的速率控制机制,
我们已经手动分配每个组件的总比特率
的LDI帧。它的四个组成部分已编码的
拟议methods.We分配约70%的
的总比特率北环线,20%的颜色和深度组件,
和10%的残差数据。从我们的实验结果,
我们观察到,该方法方面的好处
编码效率,因为它表现出更好的PSNR比曲线
锚和一些其他的处理与颜色分量只,
即使它包含所有的编码位深度,北环线和
残余以及彩色数据。
有几个问题要考虑在未来的实验。
首先,北环线和质量之间的关系
重建的意见,要仔细分析。其次,
形状自适应变换,如形状自适应离散余弦/
可以使用小波变换编码的LDI的数据,因为
H.264/AVC支持,只有4×4整数变换。最后,
构建LDI的时间之间的预测方案
帧可以利用更多的测试序列
深入的信息。遗留问题的LDI为基础的方法
如何选择适当的盖印图层像素,以填补目前的
像素的位置,如何动态地分配总位数为每个
成分,例如,颜色,深度,北环线,和残差,以及如何
深度编码的性能进行比较。由于LDI框架
包含深度信息和PSNR值可能不
评估的深度编码性能最好的措施,
我们需要制定适当的比较指标,考虑视图
利用深度信息的生成结果。
另外,在图图16中,锚定装置的所有视图分开进行编码
使用H.264/AVC与指定的参数[23]。所有
PSNR曲线所代表的平均值在各方面的意见。除
锚定编码结果,它是不可能计算
确切的比特率,因为所有的数据都合并到每个视图
一个比特流。我们已计算出的总比特率每LDI
帧,并且把它的视图数,因为每个LDI
帧包含所有观点的数据分层。由于
没有实现的LDI帧的速率控制机制,
我们已经手动分配每个组件的总比特率
的LDI帧。它的四个组成部分已编码的
拟议methods.We分配约70%的
的总比特率北环线,20%的颜色和深度组件,
和10%的残差数据。从我们的实验结果,
我们观察到,该方法方面的好处
编码效率,因为它表现出更好的PSNR比曲线
锚和一些其他的处理与颜色分量只,
即使它包含所有的编码位深度,北环线和
残余以及彩色数据。
有几个问题要考虑在未来的实验。
首先,北环线和质量之间的关系
重建的意见,要仔细分析。其次,
形状自适应变换,如形状自适应离散余弦/
可以使用小波变换编码的LDI的数据,因为
H.264/AVC支持,只有4×4整数变换。最后,
构建LDI的时间之间的预测方案
帧可以利用更多的测试序列
深入的信息。遗留问题的LDI为基础的方法
如何选择适当的盖印图层像素,以填补目前的
像素的位置,如何动态地分配总位数为每个
成分,例如,颜色,深度,北环线,和残差,以及如何
深度编码的性能进行比较。由于LDI框架
包含深度信息和PSNR值可能不
评估的深度编码性能最好的措施,
我们需要制定适当的比较指标,考虑视图
利用深度信息的生成结果。
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