385 research outputs found
A Subband Coding Method for HDTV
This paper introduces a new HDTV coder based on motion compensation, subband coding, and high order conditional entropy coding. The proposed coder exploits the temporal and spatial statistical dependencies inherent in the HDTV signal by using intra- and inter-subband conditioning for coding both the motion coordinates and the residual signal. The new framework provides an easy way to control the system complexity and performance, and inherently supports multiresolution transmission. Experimental results show that the coder outperforms MPEG-2, while still maintaining relatively low complexity
HEVC based Mixed-resolution Stereo Video Coding for Low Bitrate Transmission
This paper presents a mixed resolution stereo video coding model for High Efficiency Video Codec (HEVC). The challenging aspects of mixed resolution video coding are enabling the codec to encode frames with different frame resolution/size and using decoded pictures having different frame resolution/size for referencing. These challenges are further enlarged when implemented using HEVC, since the incoming video frames are subdivided into coding tree units. The ingenuity of the proposed codec’s design, is that the information in intermediate frames are down-sampled and yet the frames can retain the original resolution. To enable random access to full resolution decoded frame in the decoded picture buffer as reference frame a downsampled version of the decoded full resolution frame is used. The test video sequences were coded using the proposed codec and standard MV-HEVC. Results show that the proposed codec gives a significantly higher coding performance over the MV- HEVC codec
Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation
Modern displays are capable of rendering video content with high dynamic
range (HDR) and wide color gamut (WCG). However, the majority of available
resources are still in standard dynamic range (SDR). As a result, there is
significant value in transforming existing SDR content into the HDRTV standard.
In this paper, we define and analyze the SDRTV-to-HDRTV task by modeling the
formation of SDRTV/HDRTV content. Our analysis and observations indicate that a
naive end-to-end supervised training pipeline suffers from severe gamut
transition errors. To address this issue, we propose a novel three-step
solution pipeline called HDRTVNet++, which includes adaptive global color
mapping, local enhancement, and highlight refinement. The adaptive global color
mapping step uses global statistics as guidance to perform image-adaptive color
mapping. A local enhancement network is then deployed to enhance local details.
Finally, we combine the two sub-networks above as a generator and achieve
highlight consistency through GAN-based joint training. Our method is primarily
designed for ultra-high-definition TV content and is therefore effective and
lightweight for processing 4K resolution images. We also construct a dataset
using HDR videos in the HDR10 standard, named HDRTV1K that contains 1235 and
117 training images and 117 testing images, all in 4K resolution. Besides, we
select five metrics to evaluate the results of SDRTV-to-HDRTV algorithms. Our
final results demonstrate state-of-the-art performance both quantitatively and
visually. The code, model and dataset are available at
https://github.com/xiaom233/HDRTVNet-plus.Comment: Extended version of HDRTVNe
UrbanFM: Inferring Fine-Grained Urban Flows
Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem
HDTV transmission format conversion and migration path
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 77-79).by Lon E. Sunshine.Ph.D
Video Deinterlacing using Control Grid Interpolation Frameworks
abstract: Video deinterlacing is a key technique in digital video processing, particularly with the widespread usage of LCD and plasma TVs. This thesis proposes a novel spatio-temporal, non-linear video deinterlacing technique that adaptively chooses between the results from one dimensional control grid interpolation (1DCGI), vertical temporal filter (VTF) and temporal line averaging (LA). The proposed method performs better than several popular benchmarking methods in terms of both visual quality and peak signal to noise ratio (PSNR). The algorithm performs better than existing approaches like edge-based line averaging (ELA) and spatio-temporal edge-based median filtering (STELA) on fine moving edges and semi-static regions of videos, which are recognized as particularly challenging deinterlacing cases. The proposed approach also performs better than the state-of-the-art content adaptive vertical temporal filtering (CAVTF) approach. Along with the main approach several spin-off approaches are also proposed each with its own characteristics.Dissertation/ThesisM.S. Electrical Engineering 201
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