Practical Full Resolution Learned Lossless Image Compression

We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000. At the core of our method is a fully parallelizable hierarchical probabilistic model for adaptive entropy coding which is optimized end-to-end for the compression task. In contrast to recent autoregressive discrete probabilistic models such as PixelCNN, our method i) models the image distribution jointly with learned auxiliary representations instead of exclusively modeling the image distribution in RGB space, and ii) only requires three forward-passes to predict all pixel probabilities instead of one for each pixel. As a result, L3C obtains over two orders of magnitude speedups when sampling compared to the fastest PixelCNN variant (Multiscale-PixelCNN). Furthermore, we find that learning the auxiliary representation is crucial and outperforms predefined auxiliary representations such as an RGB pyramid significantly.Comment: Updated preprocessing and Table 1, see A.1 in supplementary. Code and models: https://github.com/fab-jul/L3C-PyTorc

Extreme Image Compression with Deep Learning Autoencoder

Image compression can save billions of dollars in the industry by reducing the bits needed to store and transfer an image without significantly losing visual quality. Traditional image compression methods use transform, quantization, predictive coding and entropy coding to tackle the problem, represented by international standards like JPEG (joint photographic experts group), JPEG 2000, BPG (better portable graphics), and HEIC (high efficiency image file format). Recently, there are deep learning based image compression approaches that achieved similar or better performance compared with traditional methods, represented by autoencoder, GAN (generative adversarial networks) and super-resolution based approaches. In this paper, we built autoencoder based pipelines for extreme end-to-end image compression based on Ballé’s approach in 2017 and 2018 and improved the cost function and network structure. We replaced MSE (mean square error) with RMSE (root mean square error) in the cost function and deepened the network by adding one more hidden layer before each strided convolutional layer. The source code is available in bit.ly/deepimagecompressiongithub. Our 2018 approach outperformed Ballé’s approach in 2018, which is the state-of-the-art open source implementation in image compression using deep learning in terms of PSNR (peak signalto- noise ratio) and MS-SSIM (multi-scale structural similarity) with similar bpp (bits per pixel). It also outperformed all traditional image compression methods including JPEG, and HEIC in terms of reconstruction image quality. Regarding encoding and decoding time, our 2018 approach takes significant longer than traditional methods even with the support of GPU, this need to be measured and improved in the future. Experimental results proved that deepening network in autoencoder can effectively increase model fitting without losing generalization when applied to image compression, if the network is designed appropriately. In the future, this image compression method can be applied to video compression if encoding and decoding time can be reduced to an acceptable level. Automatic neural architecture search might also be applied to help find optimal network structure for autoencoder in image compression. Optimizer can also be replaced with trainable ones, like LSTM (long short-term memory) based optimizer. Last but not least, the cost function can also include encoding and decoding time, so that these two metrics can also be optimized during training

WAVELET BASED DATA HIDING OF DEM IN THE CONTEXT OF REALTIME 3D VISUALIZATION (Visualisation 3D Temps-Réel à Distance de MNT par Insertion de Données Cachées Basée Ondelettes)

The use of aerial photographs, satellite images, scanned maps and digital elevation models necessitates the setting up of strategies for the storage and visualization of these data. In order to obtain a three dimensional visualization it is necessary to drape the images, called textures, onto the terrain geometry, called Digital Elevation Model (DEM). Practically, all these information are stored in three different files: DEM, texture and position/projection of the data in a geo-referential system. In this paper we propose to stock all these information in a single file for the purpose of synchronization. For this we have developed a wavelet-based embedding method for hiding the data in a colored image. The texture images containing hidden DEM data can then be sent from the server to a client in order to effect 3D visualization of terrains. The embedding method is integrable with the JPEG2000 coder to accommodate compression and multi-resolution visualization. Résumé L'utilisation de photographies aériennes, d'images satellites, de cartes scannées et de modèles numériques de terrains amène à mettre en place des stratégies de stockage et de visualisation de ces données. Afin d'obtenir une visualisation en trois dimensions, il est nécessaire de lier ces images appelées textures avec la géométrie du terrain nommée Modèle Numérique de Terrain (MNT). Ces informations sont en pratiques stockées dans trois fichiers différents : MNT, texture, position et projection des données dans un système géo-référencé. Dans cet article, nous proposons de stocker toutes ces informations dans un seul fichier afin de les synchroniser. Nous avons développé pour cela une méthode d'insertion de données cachées basée ondelettes dans une image couleur. Les images de texture contenant les données MNT cachées peuvent ensuite être envoyées du serveur au client afin d'effectuer une visualisation 3D de terrains. Afin de combiner une visualisation en multirésolution et une compression, l'insertion des données cachées est intégrable dans le codeur JPEG 2000

Wavelet-Based Embedded Rate Scalable Still Image Coders: A review

Embedded scalable image coding algorithms based on the wavelet transform have received considerable attention lately in academia and in industry in terms of both coding algorithms and standards activity. In addition to providing a very good coding performance, the embedded coder has the property that the bit stream can be truncated at any point and still decodes a reasonably good image. In this paper we present some state-of-the-art wavelet-based embedded rate scalable still image coders. In addition, the JPEG2000 still image compression standard is presented.

深層学習に基づく画像圧縮と品質評価

早大学位記番号:新8427早稲田大

Color image quality measures and retrieval

The focus of this dissertation is mainly on color image, especially on the images with lossy compression. Issues related to color quantization, color correction, color image retrieval and color image quality evaluation are addressed. A no-reference color image quality index is proposed. A novel color correction method applied to low bit-rate JPEG image is developed. A novel method for content-based image retrieval based upon combined feature vectors of shape, texture, and color similarities has been suggested. In addition, an image specific color reduction method has been introduced, which allows a 24-bit JPEG image to be shown in the 8-bit color monitor with 256-color display. The reduction in download and decode time mainly comes from the smart encoder incorporating with the proposed color reduction method after color space conversion stage. To summarize, the methods that have been developed can be divided into two categories: one is visual representation, and the other is image quality measure. Three algorithms are designed for visual representation: (1) An image-based visual representation for color correction on low bit-rate JPEG images. Previous studies on color correction are mainly on color image calibration among devices. Little attention was paid to the compressed image whose color distortion is evident in low bit-rate JPEG images. In this dissertation, a lookup table algorithm is designed based on the loss of PSNR in different compression ratio. (2) A feature-based representation for content-based image retrieval. It is a concatenated vector of color, shape, and texture features from region of interest (ROI). (3) An image-specific 256 colors (8 bits) reproduction for color reduction from 16 millions colors (24 bits). By inserting the proposed color reduction method into a JPEG encoder, the image size could be further reduced and the transmission time is also reduced. This smart encoder enables its decoder using less time in decoding. Three algorithms are designed for image quality measure (IQM): (1) A referenced IQM based upon image representation in very low-dimension. Previous studies on IQMs are based on high-dimensional domain including spatial and frequency domains. In this dissertation, a low-dimensional domain IQM based on random projection is designed, with preservation of the IQM accuracy in high-dimensional domain. (2) A no-reference image blurring metric. Based on the edge gradient, the degree of image blur can be measured. (3) A no-reference color IQM based upon colorfulness, contrast and sharpness

Hardware and Software Codesign of a JPEG2000 Watermarking Encoder

Analog technology has been around for a long time. The use of analog technology is necessary since we live in an analog world. However, the transmission and storage of analog technology is more complicated and in many cases less efficient than digital technology. Digital technology, on the other hand, provides fast means to be transmitted and stored. Digital technology continues to grow and it is more widely used than ever before. However, with the advent of new technology that can reproduce digital documents or images with unprecedented accuracy, it poses a risk to the intellectual rights of many artists and also on personal security. One way to protect intellectual rights of digital works is by embedding watermarks in them. The watermarks can be visible or invisible depending on the application and the final objective of the intellectual work. This thesis deals with watermarking images in the discrete wavelet transform domain. The watermarking process was done using the JPEG2000 compression standard as a platform. The hardware implementation was achieved using the ALTERA DSP Builder and SIMULINK software to program the DE2 ALTERA FPGA board. The JPEG2000 color transform and the wavelet transformation blocks were implemented using the hardware-in-the-loop (HIL) configuration