Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

IEEE Transactions on Pattern Analysis and Machine Intelligence

Modern image inpainting systems, despite the significant progress, often struggle with mask selection and holes filling. Based on Segment-Anything Model (SAM), we make the first attempt to the mask-free image inpainting and propose a new paradigm of ``clicking and filling'', which is named as Inpaint Anything (IA). The core idea behind IA is to combine the strengths of different models in order to build a very powerful and user-friendly pipeline for solving inpainting-related problems. IA supports three main features: (i) Remove Anything: users could click on an object and IA will remove it and smooth the ``hole'' with the context; (ii) Fill Anything: after certain objects removal, users could provide text-based prompts to IA, and then it will fill the hole with the corresponding generative content via driving AIGC models like Stable Diffusion; (iii) Replace Anything: with IA, users have another option to retain the click-selected object and replace the remaining background with the newly generated scenes. We are also very willing to help everyone share and promote new projects based on our Inpaint Anything (IA). Our codes are available at https://github.com/geekyutao/Inpaint-Anything.

Inpaint Anything: Segment Anything Meets Image Inpainting

No-reference bitstream-layer video quality assessment is very important and practical for monitoring the perceptual experience of end users and facilitating network maintenance. For pervasive Internet Protocol Television and mobile streaming services, in addition to quality degradation due to lossy compression, the unreliable transmission mechanism (i.e., User Datagram Protocol/IP) often leads to quality degradation due to packet loss. Different technical solutions bring in different types of visual artifacts. In this paper, we proposed a hybrid distortion ranking (HDR)-based bitstream-layer quality assessment model, whose artifact combination framework is based on the ranked linear combination operation. The model can predict the perceived quality of a video with sufficient accuracy when the video is distorted by compression artifacts, slicing artifacts, freezing (with frame skipping) artifacts, or their combinations. The core algorithms of the model were adopted into ITU-T Recommendations, P.1202.1 and P.1202.2. Furthermore, with respect to the three different types of artifacts, we compared the proposed no-reference HDR model with some state-of-the-art full-reference perceptual quality assessment models including Video Quality Model (i.e., ITU-T Rec. J.144), structural similarity (SSIM), multiscale SSIM, visual information fidelity, and the widely used metric, peak signal-to-noise ratio. We also compared our HDR model with the top performing no-reference models including Blind/Referenceless Image Spatial Quality Evaluator and video Blind Prediction of Natural Video Quality. The experiment results demonstrate the efficiency of our HDR model.

Hybrid Distortion Ranking Tuned Bitstream-Layer Video Quality Assessment

The invention is related to lossless compression of prefix- suffix-codes wherein a prefix comprises unary code, and to corresponding decompression. The method for lossless compressing prefix-suffix-codes (VLCl, VLC2, VLC3) comprises the steps of forming a first contiguous bit sequence (FBS) from the prefixes (PRFXl, PRFX2, PRFX3), and lossless compressing the first contiguous bit sequence (FBS) by removing redundancy related to the difference between the first value's frequency in the first contiguous bit sequence (FBS) and the second value's frequency in the first contiguous bit sequence (FBS). Bit values are unevenly distributed in the prefixes while distribution of bit values in the suffixes is more even. Therefore, better compression is achievable if the prefixes are compressed separately.

Method for lossless compressing prefix-suffix-codes, method for decompressing a bit sequence representing integers or symbols encoded in compressed prefix-suffix-codes and storage medium or signal carrying compressed prefix-suffix-codes

Objective quality assessment of stereoscopic panoramic images becomes a challenging problem owing to the rapid growth of 360-degree contents. Different from traditional 2D image quality assessment (IQA), more complex aspects are involved in 3D omnidirectional IQA, especially unlimited field of view (FoV) and extra depth perception, which brings difficulty to evaluate the quality of experience (QoE) of 3D omnidirectional images. In this paper, we propose a multi-viewport based full-reference stereo 360 IQA model. Due to the freely changeable viewports when browsing in the head-mounted display, our proposed approach processes the image inside FoV rather than the projected one such as equirectangular projection (ERP). In addition, since overall QoE depends on both image quality and depth perception, we utilize the features estimated by the difference map between left and right views which can reflect disparity. The depth perception features along with binocular image qualities are employed to further predict the overall QoE of 3D 360 images. The experimental results on our public Stereoscopic OmnidirectionaL Image quality assessment Database (SOLID) show that the proposed method achieves a significant improvement over some well-known IQA metrics and can accurately reflect the overall QoE of perceived images.

Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports

When for video coding Intra refresh is used, which inserts Intra coded blocks into previously Inter coded pictures, an efficiently adapted rate control method is required for error resilient video coding. A method for adaptively determining a bit budget for encoding video pictures comprises pre-analyzing (31) each of the pictures of a group of pictures, wherein a relative complexity index (35) is calculated for each picture, allocating (32) bits to the pictures based on their relative complexily index (35) and encoding (33, 34) each of the pictures with the allocated number of bits. The pre-analysis (31) comprises selecting (42) pictures for Intra refresh coding, extracting (41) attention area information (47) from the selected pictures, encoding at least the macroblocks of the attention area using Intra mode, calculating for each picture a complexity index (44), and calculating from the complexity indices (44) of the pictures of the group a relative complexity index (35) for each picture. Thus, a subjectively better video quality is achieved.

Zhibo Chen

Papers

Inpaint Anything: Segment Anything Meets Image Inpainting

Hybrid Distortion Ranking Tuned Bitstream-Layer Video Quality Assessment

Method for lossless compressing prefix-suffix-codes, method for decompressing a bit sequence representing integers or symbols encoded in compressed prefix-suffix-codes and storage medium or signal carrying compressed prefix-suffix-codes

Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports

Method and apparatus for adaptively determining a bit budget for encoding video pictures