![]() The data are linearly transformed, and each component is then normalized by a pooled activity measure, computed by exponentiating a weighted sum of rectified and exponentiated components and a constant. We introduce a parametric nonlinear transformation that is well-suited for Gaussianizing data from natural images. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. ![]() 5000 of these images have high quality pixel-level annotations 20000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. More importantly, we observe a dramatic improvement in visual quality for all images at all bit rates, which is supported by objective quality estimates using MS-SSIM. Across an independent set of test images, we find that the optimized method generally exhibits better rate-distortion performance than the standard JPEG and JPEG 2000 compression methods. Unlike these models, however, the compression model must operate at any given point along the rate-distortion curve, as specified by a trade-off parameter. Under certain conditions, the relaxed loss function may be interpreted as the log likelihood of a generative model, as implemented by a variational autoencoder. Using a variant of stochastic gradient descent, we jointly optimize the entire model for rate-distortion performance over a database of training images, introducing a continuous proxy for the discontinuous loss function arising from the quantizer. Unlike most convolutional neural networks, the joint nonlinearity is chosen to implement a form of local gain control, inspired by those used to model biological neurons. The transforms are constructed in three successive stages of convolutional linear filters and nonlinear activation functions. We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation. Furthermore, it is shown that the GAN-based NCN architecture achieves the best coding performance and even out-performs the recently standardized Versatile Video Coding (VVC) for the given scenario. Based on that analysis, we find that networks with leaky ReLU as non-linearity and training with SSIM as distortion criteria results in the highest coding gains for the VCM task. The compression performance is measured by the weighted average precision for the Cityscapes dataset. Therefore, we build-up an evaluation framework analyzing the performance of four state-of-the-art NCNs, when a Mask R-CNN is segmenting objects from the decoded image. Thus, it is reasonable to consider such NCNs, when the information sink at the decoder side is a neural network as well. However, neural compression networks (NCNs) have made an enormous progress in coding images over the last years. Several approaches already exist improving classic hybrid codecs for this task. Video and image coding for machines (VCM) is an emerging field that aims to develop compression methods resulting in optimal bitstreams when the decoded frames are analyzed by a neural network.
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