Image compression method based on learned lifting-based dwt and learned zerotree-like entropy model

Şahin, Uğur Berk
The success of deep learning in computer vision has sparked great interest in investigating deep learning-based algorithms also in many image processing applications, including image compression. The most popular end-to-end learned image compression approaches are based on auto-encoder architectures, where the image is mapped via convolutional neural networks (CNNs) into a transform (latent) representation that is quantized and processed again with CNNs to obtain the reconstructed image. The quantized latent representation is entropy coded to obtain a compressed bitstream. To have efficient entropy coding, the probability distribution of the quantized latent representation is also modeled with CNNs. The entire system, including the auto-encoder and the probability model of the latent representation, is trained jointly to minimize the rate-distortion cost function.


Image resolution enhancement using wavelet domain Hidden Markov Tree and coefficient sign estimation
Temizel, Alptekin (2007-01-01)
Image resolution enhancement using wavelets is a relatively new subject and many new algorithms have been proposed recently. These algorithms assume that the low resolution image is the approximation subband of a higher resolution image and attempts to estimate the unknown detail coefficients to reconstruct a high resolution image. A subset of these recent approaches utilized probabilistic models to estimate these unknown coefficients. Particularly, hidden Markov tree (HMT) based methods using Gaussian mixt...
A pixel-by-pixel learned lossless image compression method with parallel decoding
Gümüş, Sinem; Kamışlı, Fatih; Department of Electrical and Electronics Engineering (2022-7)
The success of deep learning in computer vision applications has led to the use of learning based algorithms also in image compression. Learning based lossless image compression algorithms can be divided into three categories, namely, pixel-by-pixel (or masked convolution based) algorithms, prior based algorithms and latent representation based algorithms. In the pixel-by-pixel algorithms, each pixel’s probability distribution is obtained by processing the previously coded left and upper neighbouring pixels...
Data-driven image captioning via salient region discovery
Kilickaya, Mert; Akkuş, Burak Kerim; Çakıcı, Ruket; Erdem, Aykut; Erdem, Erkut; İKİZLER CİNBİŞ, NAZLI (Institution of Engineering and Technology (IET), 2017-09-01)
n the past few years, automatically generating descriptions for images has attracted a lot of attention in computer vision and natural language processing research. Among the existing approaches, data-driven methods have been proven to be highly effective. These methods compare the given image against a large set of training images to determine a set of relevant images, then generate a description using the associated captions. In this study, the authors propose to integrate an object-based semantic image r...
End-to-end learned image compression with conditional latent space modelling for entropy coding
Yeşilyurt, Aziz Berkay; Kamışlı, Fatih; Department of Electrical and Electronics Engineering (2019)
This thesis presents a lossy image compression system based on an end-to-end trainable neural network. Traditional compression algorithms use linear transformation, quantization and entropy coding steps that are designed based on simple models of the data and are aimed to be low complexity. In neural network based image compression methods, the processing steps, such as transformation and entropy coding, are performed using neural networks. The use of neural networks enables transforms or probability models...
Position estimation for timing belt drives of precision machinery using structured neural networks
KILIÇ, Ergin; DOĞRUER, CAN ULAŞ; Dölen, Melik; Koku, Ahmet Buğra (2012-05-01)
This paper focuses on a viable position estimation scheme for timing-belt drives using artificial neural networks. In this study, the position of a carriage (load) is calculated via a structured neural network topology accepting input from a position sensor on the actuator side of the timing belt. The paper presents a detailed discussion on the source of transmission errors. The characteristics of the error in different operation regimes are exploited to construct different network topologies. That is, a re...
Citation Formats
U. B. Şahin, “Image compression method based on learned lifting-based dwt and learned zerotree-like entropy model,” M.S. - Master of Science, Middle East Technical University, 2022.