Lossy Image Compression with Compressive Autoencoders

We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algo- rithms which are more flexible than existing codecs. Autoencoders have the po- tential to address this need, but are difficult to optimize directly due to the inherent non-differentiabilty of the compression loss. We here show that minimal changes to the loss are sufficient to train deep autoencoders competitive with JPEG 2000 and outperforming recently proposed approaches based on RNNs. Our network is furthermore computationally efficient thanks to a sub-pixel architecture, which makes it suitable for high-resolution images. This is in contrast to previous work on autoencoders for compression using coarser approximations, shallower archi- tectures, computationally expensive methods, or focusing on small image

Piano Genie

We present Piano Genie, an intelligent controller which allows non-musicians to improvise on the piano. With Piano Genie, a user performs on a simple interface with eight buttons, and their performance is decoded into the space of plausible piano music in real time. To learn a suitable mapping procedure for this problem, we train recurrent neural network autoencoders with discrete bottlenecks: an encoder learns an appropriate sequence of buttons corresponding to a piano piece, and a decoder learns to map this sequence back to the original piece. During performance, we substitute a user's input for the encoder output, and play the decoder's prediction each time the user presses a button. To improve the intuitiveness of Piano Genie's performance behavior, we impose musically meaningful constraints over the encoder's outputs.Comment: Published as a conference paper at ACM IUI 201

IMPLEMENTATION OF QUANTITATION TECHNIQUES TO PERFORM RGB IMAGE COMPRESSION

the use of RGB images is a necessity in various fields. However, its use is constrained by the large file capacity, but it is possible to compress the images that are owned as needed. With the quantization method, the R matrix, G matrix and B matrix will be reduced in level, so that the number of bits used to represent the image is reduced. Because the number of bits is reduced, the file size becomes smaller. The quantization method is included in the Lossy Compression category, so that the compressed image cannot be decompressed again because there is missing information. Image compression is an image compression process that aims to reduce duplication of data in the image so that less memory is used to represent the image than the original image representation. There are factors why the image compression process is very appropriate so that there is no significant correlation between pixels and neighboring pixel

Task-specific color spaces and compression for machine-based object recognition

An image is compressed to reduce the memory or bandwidth it occupies. Compression is presently carried out such that the reconstructed (decompressed) image is faithful to the original image. In some recent contexts, images are generated that are not necessarily intended for human viewership. For example, such images are generated for the purposes of machine-based tasks such as action-detection, scene-recognition, etc. In such cases, compression that is driven by fidelity of the decompressed image to the original can be sub-optimal. This disclosure describes techniques to compress images based on the end use of the image. For example, if an image is used for the purposes of detecting particular objects within it, then image compression is driven by an object detector. Portions of the image that are irrelevant to detecting the sought objects are excised during compression. The result is a more efficient, task-specific, encoding of the image