78 research outputs found
Dense light field coding: a survey
Light Field (LF) imaging is a promising solution for providing more immersive and closer to reality multimedia experiences to end-users with unprecedented creative freedom and flexibility for applications in different areas, such as virtual and augmented reality. Due to the recent technological advances in optics, sensor manufacturing and available transmission bandwidth, as well as the investment of many tech giants in this area, it is expected that soon many LF transmission systems will be available to both consumers and professionals. Recognizing this, novel standardization initiatives have recently emerged in both the Joint Photographic Experts Group (JPEG) and the Moving Picture Experts Group (MPEG), triggering the discussion on the deployment of LF coding solutions to efficiently handle the massive amount of data involved in such systems.
Since then, the topic of LF content coding has become a booming research area, attracting the attention of many researchers worldwide. In this context, this paper provides a comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs. Special attention is placed on a thorough description of the different LF coding methods and on the main concepts related to this relevant area. Moreover, comprehensive insights are presented into open research challenges and future research directions for LF coding.info:eu-repo/semantics/publishedVersio
Learned Video Compression via Heterogeneous Deformable Compensation Network
Learned video compression has recently emerged as an essential research topic
in developing advanced video compression technologies, where motion
compensation is considered one of the most challenging issues. In this paper,
we propose a learned video compression framework via heterogeneous deformable
compensation strategy (HDCVC) to tackle the problems of unstable compression
performance caused by single-size deformable kernels in downsampled feature
domain. More specifically, instead of utilizing optical flow warping or
single-size-kernel deformable alignment, the proposed algorithm extracts
features from the two adjacent frames to estimate content-adaptive
heterogeneous deformable (HetDeform) kernel offsets. Then we transform the
reference features with the HetDeform convolution to accomplish motion
compensation. Moreover, we design a Spatial-Neighborhood-Conditioned Divisive
Normalization (SNCDN) to achieve more effective data Gaussianization combined
with the Generalized Divisive Normalization. Furthermore, we propose a
multi-frame enhanced reconstruction module for exploiting context and temporal
information for final quality enhancement. Experimental results indicate that
HDCVC achieves superior performance than the recent state-of-the-art learned
video compression approaches
Algorithms for compression of high dynamic range images and video
The recent advances in sensor and display technologies have brought upon the High Dynamic Range (HDR) imaging capability. The modern multiple exposure HDR sensors can achieve the dynamic range of 100-120 dB and LED and OLED display devices have contrast ratios of 10^5:1 to 10^6:1.
Despite the above advances in technology the image/video compression algorithms and associated hardware are yet based on Standard Dynamic Range (SDR) technology, i.e. they operate within an effective dynamic range of up to 70 dB for 8 bit gamma corrected images. Further the existing infrastructure for content distribution is also designed for SDR, which creates interoperability problems with true HDR capture and display equipment.
The current solutions for the above problem include tone mapping the HDR content to fit SDR. However this approach leads to image quality associated problems, when strong dynamic range compression is applied. Even though some HDR-only solutions have been proposed in literature, they are not interoperable with current SDR infrastructure and are thus typically used in closed systems.
Given the above observations a research gap was identified in the need for efficient algorithms for the compression of still images and video, which are capable of storing full dynamic range and colour gamut of HDR images and at the same time backward compatible with existing SDR infrastructure. To improve the usability of SDR content it is vital that any such algorithms should accommodate different tone mapping operators, including those that are spatially non-uniform.
In the course of the research presented in this thesis a novel two layer CODEC architecture is introduced for both HDR image and video coding. Further a universal and computationally efficient approximation of the tone mapping operator is developed and presented. It is shown that the use of perceptually uniform colourspaces for internal representation of pixel data enables improved compression efficiency of the algorithms. Further proposed novel approaches to the compression of metadata for the tone mapping operator is shown to improve compression performance for low bitrate video content. Multiple compression algorithms are designed, implemented and compared and quality-complexity trade-offs are identified. Finally practical aspects of implementing the developed algorithms are explored by automating the design space exploration flow and integrating the high level systems design framework with domain specific tools for synthesis and simulation of multiprocessor systems. The directions for further work are also presented
Visual Saliency Estimation Via HEVC Bitstream Analysis
Abstract
Since Information Technology developed dramatically from the last century 50's, digital images and video are ubiquitous. In the last decade, image and video processing have become more and more popular in biomedical, industrial, art and other fields. People made progress in the visual information such as images or video display, storage and transmission. The attendant problem is that video processing tasks in time domain become particularly arduous.
Based on the study of the existing compressed domain video saliency detection model, a new saliency estimation model for video based on High Efficiency Video Coding (HEVC) is presented. First, the relative features are extracted from HEVC encoded bitstream. The naive Bayesian model is used to train and test features based on original YUV videos and ground truth. The intra frame saliency map can be achieved after training and testing intra features. And inter frame saliency can be achieved by intra saliency with moving motion vectors. The ROC of our proposed intra mode is 0.9561. Other classification methods such as support vector machine (SVM), k nearest neighbors (KNN) and the decision tree are presented to compare the experimental outcomes. The variety of compression ratio has been analysis to affect the saliency
Advanced heterogeneous video transcoding
PhDVideo transcoding is an essential tool to promote inter-operability
between different video communication systems. This thesis presents
two novel video transcoders, both operating on bitstreams of the cur-
rent H.264/AVC standard. The first transcoder converts H.264/AVC
bitstreams to a Wavelet Scalable Video Codec (W-SVC), while the second targets the emerging High Efficiency Video Coding (HEVC).
Scalable Video Coding (SVC) enables low complexity adaptation
of compressed video, providing an efficient solution for content delivery
through heterogeneous networks. The transcoder proposed here aims at
exploiting the advantages offered by SVC technology when dealing with
conventional coders and legacy video, efficiently reusing information
found in the H.264/AVC bitstream to achieve a high rate-distortion
performance at a low complexity cost. Its main features include new
mode mapping algorithms that exploit the W-SVC larger macroblock
sizes, and a new state-of-the-art motion vector composition algorithm
that is able to tackle different coding configurations in the H.264/AVC
bitstream, including IPP or IBBP with multiple reference frames.
The emerging video coding standard, HEVC, is currently approaching the final stage of development prior to standardization. This thesis
proposes and evaluates several transcoding algorithms for the HEVC
codec. In particular, a transcoder based on a new method that is capable of complexity scalability, trading off rate-distortion performance
for complexity reduction, is proposed. Furthermore, other transcoding solutions are explored, based on a novel content-based modeling
approach, in which the transcoder adapts its parameters based on the
contents of the sequence being encoded.
Finally, the application of this research is not constrained to these
transcoders, as many of the techniques developed aim to contribute
to advance the research on this field, and have the potential to be
incorporated in different video transcoding architectures
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