![]() ![]() Delivering those types of concrete results to our customers was the motivation for this work. #Real time image compression software shrinkpic downloadThe reconstructed images from our approach delivered superior performance across the board, since our approach better preserves salient aspects in an image.Ī compression algorithm that preserves important aspects of an image at various compression rates benefits Amazon customers in several ways, such as reducing the cost of cloud storage and speeding the download of images stored with Amazon Photos. We then used these compressed images for other tasks, such as instance segmentation (finding the boundaries of objects) and object recognition. We did another experiment where we compressed images from the benchmark COCO dataset using traditional and learned image compression approaches. At a bit rate of 1.0 bits per pixel, the BPG method is the top performer. Subjects judged reconstructed images from our model as closest to the original across the three lowest bit-rates. In our paper, we report an extensive human-evaluation study that compared our approach to five other compression approaches across four different bits-per-pixel values (0.23, 0.37, 0.67, 1.0). During training, the input is both compressed and decompressed, so that we can evaluate the network according to the similarity between the original and reconstructed images, according to our new loss metric. The shorter of the two modules labeled bit string is the compressed version of the input. The architecture of our neural compression codec. Then we extracted the encoder that produces the vector representation of the input images and used it as the basis for a system that computes a similarity score (above). We split this data into training and test sets and trained a network to predict which of each pair of reconstructed images human annotators preferred. On average, the annotators spent 56 seconds on each sample. They are asked to pick the image that is closer to the original. Annotators are presented with two versions of the same image reconstructed from different compression methods (both classical and learned codecs), with the original image between them. ![]() We call this deep perceptual loss.įirst, we created a compression training set using the two-alternative forced-choice ( 2AFC) methodology. In other words, to train our image compression model, we used a loss function computed by another neural network. We drew on this observation to create a loss function suitable for training image compression models. The downstream processing normalizes the encoder outputs and computes the distance between them. F is the encoder learned from the image-ranking task. The architecture of the system we use to compute deep perceptual loss. ![]()
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