May-13-2021

今日完成:

读paper “A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms”部分内容的总结:

  1. IQA Goal: automatically assess the quality of image or videos in agreement with human quality judgement

  2. Source Image Content: resized images using bicubic interpolation

  3. Image Distortion Causes: impairments such as transmission errors and depend on the image compression scheme used.

  4. Image Distortion Types:
    1> JPEG2000 Compression: generated by compressing the reference images (full color) using JPEG2000 at bit rates ranging from 0.28 bits per pixel (bpp) to 3.15 bpp.

    2> JPEG Compression: at bit rates ranging from 0.15 bpp to 3.34 bpp.

    3> White Noise: White Gaussian noise of standard deviation 𝜎 (Value between 0.012 and 2.0) was added to R, G, B components. And the distorted components were clipped between 0 and 1, and rescaled to the range 0 to 255.

    4> Gaussian Blur: R, G, B components were filtered using a circle-symmetric 2D Gaussian kernel of standard deviation 𝜎 (0.42 to 15 pixels).

    5> Simulated fast fading Rayleigh (Wireless) channel: Images were distorted by bit errors during transmission of compressed JPEG2000 bitstream over a simulated wireless channel.

    6> Smoothness: Edge smoothness or texture blur (the received image is smoother than the original). Loss of high frequency components when compared with the original image.

    7> Blocking: appear in all block-based compression techniques. due to coarse quantization of frequency components

    observed as surface discontinuity (edge) at block boundaries, and the edges are perceived as abnormal high frequency components in the spectrum

    8> Ringing: periodic pseudo-edges around original edges, due to improper truncation of high frequency components

    9> Masking: reduction in the visibility of one image component (target), due to the presence of another (the masker)

     A. luminance masking (light adaptation)
     B. texture masking (occurs when maskers are complex textures or masker and target have similar frequencies and orientations)
    

    10> Lost block: an alteration of a pixel value, so that it doesn’t match with its neighbors pixel value.


Review下之前读SSIM后做的笔记:

Image Quality Assessment - SSIM.pdf

Question:

SSIM Figure 圈为什么是圆圈?

Answer:

Distorted image 和 中心image 距离一样 contour也是一样


明日准备:

重新review下宝亮师兄之前让我看过的2篇Screen Contents IQA 2篇论文

然后run下宝亮师兄的代码,在SIQAD数据集上看效果


Read 10 papers using traditional methods and 5 papers using deep learning methods


  1. SSIM paper的Figure 4为什么是圈?

  2. 问题1:SSIM比MSE好在哪?MSE的问题是什么?(很基础的问题)

    MSE 相减后 squared后sum

    Reference patch 和 Distorted patch 的(相减后 squared后sum 相等) 就认为MSE就相等。

    但SSIM 告诉我们:A和A’ distance不仅仅和它们之间的距离有关,而且和他们本身的特性有关

    (最本质理解图像质量)A和B是不同content,虽然A到A‘之间的距离相等,B到B’之间的距离相等,但是它们的perceptual content不同,

    所以叫做adaptive to local content

    问题2:Feature Domain的SSIM —> DISIS

  3. 问题3:SSIM代码:local patch的mean和variance :7X7, 9X9, 11X11

    为什么算local的structural similarity?

  4. No reference说Natural Scene statistics (NSS) 但是 Full Reference不说NSS

  5. 做完了SSIM后,VGG domain (Feature Domain)能不能算SSIM 算的话 代表什么物理意义?

  6. VGG做完, 在做Transformer? 基础的东西,得搞清楚。Deep Learning Features别人没有做的实验 咋们要做 try下 再做fancy的东西

  7. 每天工作12小时,不会说这一天没有汇报的东西 每周开会 “老师 我看到它,它为什么这么做,我是怎么理解想这个问题的”

  8. “为什么这个东西适合graph 为什么适合Transformer?“

  9. 在CityU做研究,而不是工程性的东西 提出问题(他为什么这么做), try our best去解答

  10. 把宝亮师兄想要做的想明白,然后按照师兄的想法去做,思路好,结果可能好;思路不好,结果可能不好。(什么正确的做实验的方式)

  11. 看到efforts,保证每天12个小时工作时间。

  12. 做别人替代不了的东西!打基础 踩坑 进去到领域。