Summative Stereoscopic Image Compression using Arithmetic Coding

Image compression targets at plummeting the amount of bits required for image representation for save storage space and speed up the transmission over network. The reduction of size helps to store more images in the disk and take less transfer time in the data network. Stereoscopic image refers to a three dimensional (3D) image that is perceived by the human brain as the transformation of two images that is being sent to the left and right human eyes with distinct phases. However, storing of these images takes twice space than a single image and hence the motivation for this novel approach called Summative Stereoscopic Image Compression using Arithmetic Coding (S2ICAC) where the difference and average of these stereo pair images are calculated, quantized in the case of lossy approach and unquantized in the case of lossless approach, and arithmetic coding is applied. The experimental result analysis indicates that the proposed method achieves high compression ratio and high PSNR value. The proposed method is also compared with JPEG 2000 Position Based Coding Scheme(JPEG 2000 PBCS) and Stereoscopic Image Compression using Huffman Coding (SICHC). From the experimental analysis, it is observed that S2ICAC outperforms JPEG 2000 PBCS as well as SICHC

Efficient Learning-based Image Enhancement : Application to Compression Artifact Removal and Super-resolution

Many computer vision and computational photography applications essentially solve an image enhancement problem. The image has been deteriorated by a specific noise process, such as aberrations from camera optics and compression artifacts, that we would like to remove. We describe a framework for learning-based image enhancement. At the core of our algorithm lies a generic regularization framework that comprises a prior on natural images, as well as an application-specific conditional model based on Gaussian processes. In contrast to prior learning-based approaches, our algorithm can instantly learn task-specific degradation models from sample images which enables users to easily adapt the algorithm to a specific problem and data set of interest. This is facilitated by our efficient approximation scheme of large-scale Gaussian processes. We demonstrate the efficiency and effectiveness of our approach by applying it to example enhancement applications including single-image super-resolution, as well as artifact removal in JPEG- and JPEG 2000-encoded images

Motion Scalability for Video Coding with Flexible Spatio-Temporal Decompositions

PhDThe research presented in this thesis aims to extend the scalability range of the wavelet-based video coding systems in order to achieve fully scalable coding with a wide range of available decoding points. Since the temporal redundancy regularly comprises the main portion of the global video sequence redundancy, the techniques that can be generally termed motion decorrelation techniques have a central role in the overall compression performance. For this reason the scalable motion modelling and coding are of utmost importance, and specifically, in this thesis possible solutions are identified and analysed. The main contributions of the presented research are grouped into two interrelated and complementary topics. Firstly a flexible motion model with rateoptimised estimation technique is introduced. The proposed motion model is based on tree structures and allows high adaptability needed for layered motion coding. The flexible structure for motion compensation allows for optimisation at different stages of the adaptive spatio-temporal decomposition, which is crucial for scalable coding that targets decoding on different resolutions. By utilising an adaptive choice of wavelet filterbank, the model enables high compression based on efficient mode selection. Secondly, solutions for scalable motion modelling and coding are developed. These solutions are based on precision limiting of motion vectors and creation of a layered motion structure that describes hierarchically coded motion. The solution based on precision limiting relies on layered bit-plane coding of motion vector values. The second solution builds on recently established techniques that impose scalability on a motion structure. The new approach is based on two major improvements: the evaluation of distortion in temporal Subbands and motion search in temporal subbands that finds the optimal motion vectors for layered motion structure. Exhaustive tests on the rate-distortion performance in demanding scalable video coding scenarios show benefits of application of both developed flexible motion model and various solutions for scalable motion coding