3/31/2023 0 Comments Photo batch jpg compression![]() ![]() Then, go directly to the Perform JPEG Deblocking Using DnCNN Network section in this example. If you do not want to download the training data or train the network, then you can load the pretrained DnCNN network by typing load("trainedJPEGDnCNN.mat") at the command line. The data set includes photos of people, animals, cities, and more. Download Training Dataĭownload the IAPR TC-12 Benchmark, which consists of 20,000 still natural images. Once the DnCNN network learns how to estimate a residual image, it can reconstruct an undistorted version of a compressed JPEG image by adding the residual image to the compressed luminance channel, then converting the image back to the RGB color space. If Y Original is the luminance of the pristine image and Y Compressed is the luminance of the image containing JPEG compression artifacts, then the input to the DnCNN network is Y Compressed and the network learns to predict Y Residual = Y Compressed - Y Original from the training data. DnCNN is trained using only the luminance channel because human perception is more sensitive to changes in brightness than changes in color. In contrast, the two chrominance channels of an image, Cb and Cr, are different linear combinations of the red, green, and blue pixel values that represent color-difference information. The luminance channel of an image, Y, represents the brightness of each pixel through a linear combination of the red, green, and blue pixel values. The DnCNN network is trained to detect the residual image from the luminance of a color image. For this example, distortion appears as JPEG blocking artifacts. The residual image contains information about the image distortion. A residual image is the difference between a pristine image and a distorted copy of the image. The reference paper employs a residual learning strategy, meaning that the DnCNN network learns to estimate the residual image. However, the DnCNN architecture can also be trained to remove JPEG compression artifacts or increase image resolution. The network was primarily designed to remove noise from images. This example uses a built-in deep feed-forward convolutional neural network, called DnCNN. ![]()
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