Decompressor images4/28/2024 ![]() ![]() # Mean absolute difference across pixels.ĭistortion = tf.reduce_mean(abs(x - x_tilde)) x_tilde = make_synthesis_transform()(y_tilde) Obviously, with the transforms untrained, the reconstruction is not very useful. Distortion is the error between original image and reconstruction. Lastly, the noisy latents are passed back through the synthesis transform to produce an image reconstruction \(\tilde x\). Rate: tf.Tensor(, shape=(1,), dtype=float32) Y_tilde, rate = entropy_model(y, training=True) entropy_model = tfc.ContinuousBatchedEntropyModel( prior = tfc.NoisyLogistic(loc=0., scale=tf.linspace(.01, 2., 10))ĭuring training, tfc.ContinuousBatchedEntropyModel adds uniform noise, and uses the noise and the prior to compute a (differentiable) upper bound on the rate (the average number of bits necessary to encode the latent representation). As the scale approaches zero, a logistic distribution approaches a dirac delta (spike), but the added noise causes the "noisy" distribution to approach the uniform distribution instead. tfc.NoisyLogistic accounts for the fact that the latents have additive noise. For example, it could be a set of independent logistic distributions with different scales for each latent dimension. The "prior" is a probability density that we train to model the marginal distribution of the noisy latents. y_tilde = y + tf.random.uniform(y.shape, -.5. This is the same terminology as used in the paper End-to-end Optimized Image Compression. To model this in a differentiable way during training, we add uniform noise in the interval \((-.5. The latents will be quantized at test time. To get the latent representation \(y\), we need to cast it to float32, add a batch dimension, and pass it through the analysis transform. # Installs the latest version of TFC compatible with the installed TF version. More background on learned data compression can be found in this paper targeted at people familiar with classical data compression, or this survey targeted at a machine learning audience. The method is based on the paper End-to-end Optimized Image Compression. The examples below use an autoencoder-like model to compress images from the MNIST dataset. Lossy compression involves making a trade-off between rate, the expected number of bits needed to encode a sample, and distortion, the expected error in the reconstruction of the sample. The algorithm exhibits a significant improvement in bit savings of 76%, 78%, 75% & 74% over JPEG-LS, JP2K-LM, CALIC and recent neural network approach respectively, making it a good compression-decompression technique.This notebook shows how to do lossy data compression using neural networks and TensorFlow Compression. The empirical results indicate that the proposed work outperformed other neural network related compression technique for medical images by approximately 35%, 10% and 5% in PSNR, Color SSIM and MS-SSIM respectively. ![]() Color-SSIM has been exclusively used to check the quality of the chrominance part of the cell images after decompression. The two latent space representations (first for the original image and second for the residual image) are used to rebuild the final original image. The proposed residual-based dual autoencoder network is trained to extract the unique features which are then used to reconstruct the original image through the decompressor module. We know that the medical images used for disease diagnosis are around multiple gigabytes size, which is quite huge. In this work, we propose a two-stage autoencoder based compressor-decompressor framework for compressing malaria RBC cell image patches. ![]()
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