Geometry-based spherical JND modeling for 360∘^\circ display

360$^\circ$ videos have received widespread attention due to its realistic and immersive experiences for users. To date, how to accurately model the user perceptions on 360$^\circ$ display is still a challenging issue. In this paper, we exploit the visual characteristics of 360$^\circ$ projection and display and extend the popular just noticeable difference (JND) model to spherical JND (SJND). First, we propose a quantitative 2D-JND model by jointly considering spatial contrast sensitivity, luminance adaptation and texture masking effect. In particular, our model introduces an entropy-based region classification and utilizes different parameters for different types of regions for better modeling performance. Second, we extend our 2D-JND model to SJND by jointly exploiting latitude projection and field of view during 360$^\circ$ display. With this operation, SJND reflects both the characteristics of human vision system and the 360$^\circ$ display. Third, our SJND model is more consistent with user perceptions during subjective test and also shows more tolerance in distortions with fewer bit rates during 360$^\circ$ video compression. To further examine the effectiveness of our SJND model, we embed it in Versatile Video Coding (VVC) compression. Compared with the state-of-the-arts, our SJND-VVC framework significantly reduced the bit rate with negligible loss in visual quality

A comprehensive video codec comparison

In this paper, we compare the video codecs AV1 (version 1.0.0-2242 from August 2019), HEVC (HM and x265), AVC (x264), the exploration software JEM which is based on HEVC, and the VVC (successor of HEVC) test model VTM (version 4.0 from February 2019) under two fair and balanced configurations: All Intra for the assessment of intra coding and Maximum Coding Efficiency with all codecs being tuned for their best coding efficiency settings. VTM achieves the highest coding efficiency in both configurations, followed by JEM and AV1. The worst coding efficiency is achieved by x264 and x265, even in the placebo preset for highest coding efficiency. AV1 gained a lot in terms of coding efficiency compared to previous versions and now outperforms HM by 24% BD-Rate gains. VTM gains 5% over AV1 in terms of BD-Rates. By reporting separate numbers for JVET and AOM test sequences, it is ensured that no bias in the test sequences exists. When comparing only intra coding tools, it is observed that the complexity increases exponentially for linearly increasing coding efficiency

Analysis of Affine Motion-Compensated Prediction in Video Coding

Motion-compensated prediction is used in video coding standards like High Efficiency Video Coding (HEVC) as one key element of data compression. Commonly, a purely translational motion model is employed. In order to also cover non-translational motion types like rotation or scaling (zoom), e. g. contained in aerial video sequences such as captured from unmanned aerial vehicles (UAV), an affine motion model can be applied. In this work, a model for affine motion-compensated prediction in video coding is derived. Using the rate-distortion theory and the displacement estimation error caused by inaccurate affine motion parameter estimation, the minimum required bit rate for encoding the prediction error is determined. In this model, the affine transformation parameters are assumed to be affected by statistically independent estimation errors, which all follow a zero-mean Gaussian distributed probability density function (pdf). The joint pdf of the estimation errors is derived and transformed into the pdfof the location-dependent displacement estimation error in the image. The latter is related to the minimum required bit rate for encoding the prediction error. Similar to the derivations of the fully affine motion model, a four-parameter simplified affine model is investigated. Both models are of particular interest since they are considered for the upcoming video coding standard Versatile Video Coding (VVC) succeeding HEVC. Both models provide valuable information about the minimum bit rate for encoding the prediction error as a function of affine estimation accuracies. © 1992-2012 IEEE

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

JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS

The massive volume of data generated daily by the gathering of medical images with different modalities might be difficult to store in medical facilities and share through communication networks. To alleviate this issue, efficient compression methods must be implemented to reduce the amount of storage and transmission resources required in such applications. However, since the preservation of all image details is highly important in the medical context, the use of lossless image compression algorithms is of utmost importance. This thesis presents the research results on a lossless compression scheme designed to encode both computerized tomography (CT) and positron emission tomography (PET). Different techniques, such as image-to-image translation, intra prediction, and inter prediction are used. Redundancies between both image modalities are also investigated. To perform the image-to-image translation approach, we resort to lossless compression of the original CT data and apply a cross-modality image translation generative adversarial network to obtain an estimation of the corresponding PET. Two approaches were implemented and evaluated to determine a PET residue that will be compressed along with the original CT. In the first method, the residue resulting from the differences between the original PET and its estimation is encoded, whereas in the second method, the residue is obtained using encoders inter-prediction coding tools. Thus, in alternative to compressing two independent picture modalities, i.e., both images of the original PET-CT pair solely the CT is independently encoded alongside with the PET residue, in the proposed method. Along with the proposed pipeline, a post-processing optimization algorithm that modifies the estimated PET image by altering the contrast and rescaling the image is implemented to maximize the compression efficiency. Four different versions (subsets) of a publicly available PET-CT pair dataset were tested. The first proposed subset was used to demonstrate that the concept developed in this work is capable of surpassing the traditional compression schemes. The obtained results showed gains of up to 8.9% using the HEVC. On the other side, JPEG2k proved not to be the most suitable as it failed to obtain good results, having reached only -9.1% compression gain. For the remaining (more challenging) subsets, the results reveal that the proposed refined post-processing scheme attains, when compared to conventional compression methods, up 6.33% compression gain using HEVC, and 7.78% using VVC

On Sparse Coding as an Alternate Transform in Video Coding

In video compression, specifically in the prediction process, a residual signal is calculated by subtracting the predicted from the original signal, which represents the error of this process. This residual signal is usually transformed by a discrete cosine transform (DCT) from the pixel, into the frequency domain. It is then quantized, which filters more or less high frequencies (depending on a quality parameter). The quantized signal is then entropy encoded usually by a context-adaptive binary arithmetic coding engine (CABAC), and written into a bitstream. In the decoding phase the process is reversed. DCT and quantization in combination are efficient tools, but they are not performing well at lower bitrates and creates distortion and side effect. The proposed method uses sparse coding as an alternate transform which compresses well at lower bitrates, but not well at high bitrates. The decision which transform is used is based on a rate-distortion optimization (RDO) cost calculation to get both transforms in their optimal performance range. The proposed method is implemented in high efficient video coding (HEVC) test model HM-16.18 and high efficient video coding for screen content coding (HEVC-SCC) for test model HM-16.18+SCM-8.7, with a Bjontegaard rate difference (BD-rate) saving, which archives up to 5.5%, compared to the standard

Learned-based Intra Coding Tools for Video Compression.

PhD Theses.The increase in demand for video rendering in 4K and beyond displays, as well as immersive video formats, requires the use of e cient compression techniques. In this thesis novel methods for enhancing the e ciency of current and next generation video codecs are investigated. Several aspects that in uence the way conventional video coding methods work are considered. The methods proposed in this thesis utilise Neural Networks (NNs) trained for regression tasks in order to predict data. In particular, Convolutional Neural Networks (CNNs) are used to predict Rate-Distortion (RD) data for intra-coded frames. Moreover, a novel intra-prediction methods are proposed with the aim of providing new ways to exploit redundancies overlooked by traditional intraprediction tools. Additionally, it is shown how such methods can be simpli ed in order to derive less resource-demanding tools

Deep learning and bidirectional optical flow based viewport predictions for 360° video coding

The rapid development of virtual reality applications continues to urge better compression of 360° videos owing to the large volume of content. These videos are typically converted to 2-D formats using various projection techniques in order to benefit from ad-hoc coding tools designed to support conventional 2-D video compression. Although recently emerged video coding standard, Versatile Video Coding (VVC) introduces 360° video specific coding tools, it fails to prioritize the user observed regions in 360° videos, represented by the rectilinear images called the viewports. This leads to the encoding of redundant regions in the video frames, escalating the bit rate cost of the videos. In response to this issue, this paper proposes a novel 360° video coding framework for VVC which exploits user observed viewport information to alleviate pixel redundancy in 360° videos. In this regard, bidirectional optical flow, Gaussian filter and Spherical Convolutional Neural Networks (Spherical CNN) are deployed to extract perceptual features and predict user observed viewports. By appropriately fusing the predicted viewports on the 2-D projected 360° video frames, a novel Regions of Interest (ROI) aware weightmap is developed which can be used to mask the source video and introduce adaptive changes to the Lagrange and quantization parameters in VVC. Comprehensive experiments conducted in the context of VVC Test Model (VTM) 7.0 show that the proposed framework can improve bitrate reduction, achieving an average bitrate saving of 5.85% and up to 17.15% at the same perceptual quality which is measured using Viewport Peak Signal-To-Noise Ratio (VPSNR)