In this work, we present a comparison between color spaces namely YUV, LAB,
RGB and their effect on learned image compression. For this we use the
structure and color based learned image codec (SLIC) from our prior work, which
consists of two branches - one for the luminance component (Y or L) and another
for chrominance components (UV or AB). However, for the RGB variant we input
all 3 channels in a single branch, similar to most learned image codecs
operating in RGB. The models are trained for multiple bitrate configurations in
each color space. We report the findings from our experiments by evaluating
them on various datasets and compare the results to state-of-the-art image
codecs. The YUV model performs better than the LAB variant in terms of MS-SSIM
with a Bj{\o}ntegaard delta bitrate (BD-BR) gain of 7.5\% using VTM
intra-coding mode as the baseline. Whereas the LAB variant has a better
performance than YUV model in terms of CIEDE2000 having a BD-BR gain of 8\%.
Overall, the RGB variant of SLIC achieves the best performance with a BD-BR
gain of 13.14\% in terms of MS-SSIM and a gain of 17.96\% in CIEDE2000 at the
cost of a higher model complexity.

通过使用 YUV、LAB 和 RGB 颜色空间之间的比较以及它们对学习图像压缩的影响，我们报告了通过在不同数据集上进行实验对 SLIC 模型进行评估，并将结果与最先进的图像编解码器进行比较。

关于学习图像压缩中颜色空间效应的研究

A Study on the Effect of Color Spaces in Learned Image Compression

Retinal image quality assessment (RIQA) is essential for controlling the
quality of retinal imaging and guaranteeing the reliability of diagnoses by
ophthalmologists or automated analysis systems. Existing RIQA methods focus on
the RGB color-space and are developed based on small datasets with binary
quality labels (i.e., `Accept' and `Reject'). In this paper, we first
re-annotate an Eye-Quality (EyeQ) dataset with 28,792 retinal images from the
EyePACS dataset, based on a three-level quality grading system (i.e., `Good',
`Usable' and `Reject') for evaluating RIQA methods. Our RIQA dataset is
characterized by its large-scale size, multi-level grading, and multi-modality.
Then, we analyze the influences on RIQA of different color-spaces, and propose
a simple yet efficient deep network, named Multiple Color-space Fusion Network
(MCF-Net), which integrates the different color-space representations at both a
feature-level and prediction-level to predict image quality grades. Experiments
on our EyeQ dataset show that our MCF-Net obtains a state-of-the-art
performance, outperforming the other deep learning methods. Furthermore, we
also evaluate diabetic retinopathy (DR) detection methods on images of
different quality, and demonstrate that the performances of automated
diagnostic systems are highly dependent on image quality.

本研究使用三级分级系统对一个由 28792 张来自 EyePACS 数据集的视网膜图像组成的眼睛质量（EyeQ）数据集进行了重新注释，然后分析了不同色彩空间对视网膜图像质量评估的影响，并提出了一种名为多色彩空间融合网络 (MCF-Net) 的简单而有效的深度网络，该网络将不同色彩空间表示集成到特征级和预测级来预测图像质量分级。实验证明，MCF-Net 比其他深度学习方法表现更好，并且还评估了不同质量图像上自动诊断系统的性能表现。

评估不同色彩空间中的视网膜图像质量评估网络

Evaluation of Retinal Image Quality Assessment Networks in Different  Color-spaces

Image classification is a fundamental application in computer vision.
Recently, deeper networks and highly connected networks have shown state of the
art performance for image classification tasks. Most datasets these days
consist of a finite number of color images. These color images are taken as
input in the form of RGB images and classification is done without modifying
them. We explore the importance of color spaces and show that color spaces
(essentially transformations of original RGB images) can significantly affect
classification accuracy. Further, we show that certain classes of images are
better represented in particular color spaces and for a dataset with a highly
varying number of classes such as CIFAR and Imagenet, using a model that
considers multiple color spaces within the same model gives excellent levels of
accuracy. Also, we show that such a model, where the input is preprocessed into
multiple color spaces simultaneously, needs far fewer parameters to obtain high
accuracy for classification. For example, our model with 1.75M parameters
significantly outperforms DenseNet 100-12 that has 12M parameters and gives
results comparable to Densenet-BC-190-40 that has 25.6M parameters for
classification of four competitive image classification datasets namely:
CIFAR-10, CIFAR-100, SVHN and Imagenet. Our model essentially takes an RGB
image as input, simultaneously converts the image into 7 different color spaces
and uses these as inputs to individual densenets. We use small and wide
densenets to reduce computation overhead and number of hyperparameters
required. We obtain significant improvement on current state of the art results
on these datasets as well.

探讨了颜色空间对于图像分类的影响，展示了多颜色空间模型在 CIFAR-10、CIFAR-100、SVHN 和 Imagenet 等多个数据集上都能够取得高水平的分类准确度，并且参数数量很少。