Loss-resilient Coding of Texture and Depth for Free-viewpoint Video Conferencing

Free-viewpoint video conferencing allows a participant to observe the remote 3D scene from any freely chosen viewpoint. An intermediate virtual viewpoint image is commonly synthesized using two pairs of transmitted texture and depth maps from two neighboring captured viewpoints via depth-image-based rendering (DIBR). To maintain high quality of synthesized images, it is imperative to contain the adverse effects of network packet losses that may arise during texture and depth video transmission. Towards this end, we develop an integrated approach that exploits the representation redundancy inherent in the multiple streamed videos a voxel in the 3D scene visible to two captured views is sampled and coded twice in the two views. In particular, at the receiver we first develop an error concealment strategy that adaptively blends corresponding pixels in the two captured views during DIBR, so that pixels from the more reliable transmitted view are weighted more heavily. We then couple it with a sender-side optimization of reference picture selection (RPS) during real-time video coding, so that blocks containing samples of voxels that are visible in both views are more error-resiliently coded in one view only, given adaptive blending will erase errors in the other view. Further, synthesized view distortion sensitivities to texture versus depth errors are analyzed, so that relative importance of texture and depth code blocks can be computed for system-wide RPS optimization. Experimental results show that the proposed scheme can outperform the use of a traditional feedback channel by up to 0.82 dB on average at 8% packet loss rate, and by as much as 3 dB for particular frames

Depth sequence coding with hierarchical partitioning and spatial-domain quantization

Depth coding in 3D-HEVC deforms object shapes due to block-level edge-approximation and lacks efficient techniques to exploit the statistical redundancy, due to the frame-level clustering tendency in depth data, for higher coding gain at near-lossless quality. This paper presents a standalone mono-view depth sequence coder, which preserves edges implicitly by limiting quantization to the spatial-domain and exploits the frame-level clustering tendency efficiently with a novel binary tree-based decomposition (BTBD) technique. The BTBD can exploit the statistical redundancy in frame-level syntax, motion components, and residuals efficiently with fewer block-level prediction/coding modes and simpler context modeling for context-adaptive arithmetic coding. Compared with the depth coder in 3D-HEVC, the proposed one has achieved significantly lower bitrate at lossless to near-lossless quality range for mono-view coding and rendered superior quality synthetic views from the depth maps, compressed at the same bitrate, and the corresponding texture frames. © 1991-2012 IEEE

Improved depth coding for HEVC focusing on depth edge approximation

The latest High Efficiency Video Coding (HEVC) standard has greatly improved the coding efficiency compared to its predecessor H.264. An important share of which is the adoption of hierarchical block partitioning structures and an extended number of modes. The structure of existing inter-modes is appropriate mainly to handle the rectangular and square aligned motion patterns. However, they could not be suitable for the block partitioning of depth objects having partial foreground motion with irregular edges and background. In such cases, the HEVC reference test model (HM) normally explores finer level block partitioning that requires more bits and encoding time to compensate large residuals. Since motion detection is the underlying criteria for mode selection, in this work, we use the energy concentration ratio feature of phase correlation to capture different types of motion in depth object. For better motion modeling focusing at depth edges, the proposed technique also uses an extra pattern mode comprising a group of templates with various rectangular and non-rectangular object shapes and edges. As the pattern mode could save bits by encoding only the foreground areas and beat all other inter-modes in a block once selected, the proposed technique could improve the rate-distortion performance. It could also reduce encoding time by skipping further branching using the pattern mode and selecting a subset of modes using innovative pre-processing criteria. Experimentally it could save 29% average encoding time and improve 0.10 dB Bjontegaard Delta peak signal-to-noise ratio compared to the HM

Steered mixture-of-experts for light field images and video : representation and coding

Research in light field (LF) processing has heavily increased over the last decade. This is largely driven by the desire to achieve the same level of immersion and navigational freedom for camera-captured scenes as it is currently available for CGI content. Standardization organizations such as MPEG and JPEG continue to follow conventional coding paradigms in which viewpoints are discretely represented on 2-D regular grids. These grids are then further decorrelated through hybrid DPCM/transform techniques. However, these 2-D regular grids are less suited for high-dimensional data, such as LFs. We propose a novel coding framework for higher-dimensional image modalities, called Steered Mixture-of-Experts (SMoE). Coherent areas in the higher-dimensional space are represented by single higher-dimensional entities, called kernels. These kernels hold spatially localized information about light rays at any angle arriving at a certain region. The global model consists thus of a set of kernels which define a continuous approximation of the underlying plenoptic function. We introduce the theory of SMoE and illustrate its application for 2-D images, 4-D LF images, and 5-D LF video. We also propose an efficient coding strategy to convert the model parameters into a bitstream. Even without provisions for high-frequency information, the proposed method performs comparable to the state of the art for low-to-mid range bitrates with respect to subjective visual quality of 4-D LF images. In case of 5-D LF video, we observe superior decorrelation and coding performance with coding gains of a factor of 4x in bitrate for the same quality. At least equally important is the fact that our method inherently has desired functionality for LF rendering which is lacking in other state-of-the-art techniques: (1) full zero-delay random access, (2) light-weight pixel-parallel view reconstruction, and (3) intrinsic view interpolation and super-resolution

Object-based coding for plenoptic videos

A new object-based coding system for a class of dynamic image-based representations called plenoptic videos (PVs) is proposed. PVs are simplified dynamic light fields, where the videos are taken at regularly spaced locations along line segments instead of a 2-D plane. In the proposed object-based approach, objects at different depth values are segmented to improve the rendering quality. By encoding PVs at the object level, desirable functionalities such as scalability of contents, error resilience, and interactivity with an individual image-based rendering (IBR) object can be achieved. Besides supporting the coding of texture and binary shape maps for IBR objects with arbitrary shapes, the proposed system also supports the coding of grayscale alpha maps as well as depth maps (geometry information) to respectively facilitate the matting and rendering of the IBR objects. Both temporal and spatial redundancies among the streams in the PV are exploited to improve the coding performance, while avoiding excessive complexity in selective decoding of PVs to support fast rendering speed. Advanced spatial/temporal prediction methods such as global disparity-compensated prediction, as well as direct prediction and its extensions are developed. The bit allocation and rate control scheme employing a new convex optimization-based approach are also introduced. Experimental results show that considerable improvements in coding performance are obtained for both synthetic and real scenes, while supporting the stated object-based functionalities. © 2006 IEEE.published_or_final_versio

Selected topics in video coding and computer vision

Video applications ranging from multimedia communication to computer vision have been extensively studied in the past decades. However, the emergence of new applications continues to raise questions that are only partially answered by existing techniques. This thesis studies three selected topics related to video: intra prediction in block-based video coding, pedestrian detection and tracking in infrared imagery, and multi-view video alignment.;In the state-of-art video coding standard H.264/AVC, intra prediction is defined on the hierarchical quad-tree based block partitioning structure which fails to exploit the geometric constraint of edges. We propose a geometry-adaptive block partitioning structure and a new intra prediction algorithm named geometry-adaptive intra prediction (GAIP). A new texture prediction algorithm named geometry-adaptive intra displacement prediction (GAIDP) is also developed by extending the original intra displacement prediction (IDP) algorithm with the geometry-adaptive block partitions. Simulations on various test sequences demonstrate that intra coding performance of H.264/AVC can be significantly improved by incorporating the proposed geometry adaptive algorithms.;In recent years, due to the decreasing cost of thermal sensors, pedestrian detection and tracking in infrared imagery has become a topic of interest for night vision and all weather surveillance applications. We propose a novel approach for detecting and tracking pedestrians in infrared imagery based on a layered representation of infrared images. Pedestrians are detected from the foreground layer by a Principle Component Analysis (PCA) based scheme using the appearance cue. To facilitate the task of pedestrian tracking, we formulate the problem of shot segmentation and present a graph matching-based tracking algorithm. Simulations with both OSU Infrared Image Database and WVU Infrared Video Database are reported to demonstrate the accuracy and robustness of our algorithms.;Multi-view video alignment is a process to facilitate the fusion of non-synchronized multi-view video sequences for various applications including automatic video based surveillance and video metrology. In this thesis, we propose an accurate multi-view video alignment algorithm that iteratively aligns two sequences in space and time. To achieve an accurate sub-frame temporal alignment, we generalize the existing phase-correlation algorithm to 3-D case. We also present a novel method to obtain the ground-truth of the temporal alignment by using supplementary audio signals sampled at a much higher rate. The accuracy of our algorithm is verified by simulations using real-world sequences