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Deep learning-based Video Quality Assessment
Thanks for the release of DVQA.
I have run the eval.py on my workstation, which has the following specs. But somehow I always meet the error "CUDA out of memory" when evaluating 720p and 1080p videos. Only videos with the resolution of 540p or lower worked fine . I am wondering if there is any configuration I can modify to evaluate 720p/1080p videos on my workstation, or simply I just need to upgrade the hardware.
OS: Windows 10 home
CPU: Intel i5-10400
RAM: 16GB
GPU: RTX2060 with 6GB memory
SSD: 256GB
Looking forward to your reply, Thanks a lot.
is there any plan to provide pretrained models?
I want to test the code.However,it's need a long time to edit this.Can you updownload the file (.json)about the dataset information of the LIVE and CSIQ ?Thanks very much.
我需要测试的视频是yuv大小大约是433M 1366x768 大约280多帧。使用CPU机器进行评分。机器内存4核32G。 运行一段时间。提示需要52G的内存。为何需要如此之大的内存?能否解答下。或者是否有参数可以调节。
when i test it on a video using unsharp mask filter, the mos is better, but the value of dqva is lower,the value of vmaf is higher; may be caused by the training data(more likely), or by the network (the use of residual frames);
any solution for prefilter or postprefilter?
查了资料说是主观质量评分,不确定它的意义
Very grateful the release of DVQA.
I have several questions.
I have prepared the envirionment.But I want to use by CPU.Could you tell me or add the way about how to use it by CPU instead of GPU
There are so many datasets on the official website, which one do you use for test? Thank you.
@tencent-adm RuntimeError: The size of tensor a (109) must match the size of tensor b (71) at non-singleton dimension 2
could you introduce some about the dataset size, training environments, training speed
Hi, I have trained the C3DVQA model on LIVE-VQA dataset, and the performance is very strange.
I find that in this line:
DVQA/dataset/LIVE/prep_live_score.py
Line 62 in 2172733
the mos should not be subtracted by 100.
Like what's shown in the following table:
change | SROCC | PLCC | |
---|---|---|---|
in paper | \ | 92.61 | 91.22 |
origin | 100-mos | 30.46 | 29.3 |
bug fixed | mos | 31.43 | 42.2 |
And the performance is not as well as those in paper C3DVQA.
Can you help me? I'm new to this field. Thanks a lot!
刚接触视频质量评估方面的测试,看了DVQA的README之后,还是一脸懵逼,有基础一点的使用文档吗?
包括,训练集合 验证集 测试集。谢谢
Can't run eval.py.
你好, 在复现论文的过程中,发现性能结果与论文相差较大(训练、测试集划分以及具体的学习率等均采用原始代码所给参数)。想咨询一下,具体的论文中复现性能所采用的细节。
1.首先是视频帧的选取,原始代码中所采用的是跳帧操作。但我注意到论文中的描述是 Training segments are randomly cropped from videos for data augmentation.We select a random temporal position and sample a clip with 60 frames.如果我的理解没有错误的话,这里的意思是每个视频随机选取一个60帧的连续片段。 所以,我想问一下,这个结果是按照开源代码的操作还是论文的操作实现的?亦或是我的这部分理解有问题?
2.另外,以上两种方法我都尝试过,但似乎性能仍有较大差距。在论文中提到的重复实验部分,具体是怎么重复的,如果是对于数据集重新划分后实验,能否提供10次对应的划分json文件.
Very grateful the release of DVQA
.
It seems that the provided dataset is from JND-VideoSet
. But clearly VideoSet
just provides the JND threshold to my knowledge. So, the MOS provided here is a subjectively collected result from experiment ? or a pseudo one with some tricks?
Is there a method to output the used FLOPs of the DVQA model?
Thanks in advance.
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