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Lensless imaging protects visual privacy by capturing heavily blurred images that are imperceptible for humans to recognize the subject but contain enough information for machines to infer information. Unfortunately, protecting visual privacy comes with a reduction in recognition accuracy and vice versa. We propose a learnable lensless imaging framework that protects visual privacy while maintaini...Show More
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
Ren Yang;Radu Timofte;Meisong Zheng;Qunliang Xing;Minglang Qiao;Mai Xu;Lai Jiang;Huaida Liu;Ying Chen;Youcheng Ben;Xiao Zhou;Chen Fu;Pei Cheng;Gang Yu;Junyi Li;Renlong Wu;Zhilu Zhang;Wei Shang;Zhengyao Lv;Yunjin Chen;Mingcai Zhou;Dongwei Ren;Kai Zhang;Wangmeng Zuo;Pavel Ostyakov;Vyal Dmitry;Shakarim Soltanayev;Chervontsev Sergey;Zhussip Magauiya;Xueyi Zou;Youliang Yan;Pablo Navarrete Michelini;Yunhua Lu;Diankai Zhang;Shaoli Liu;Si Gao;Biao Wu;Chengjian Zheng;Xiaofeng Zhang;Kaidi Lu;Ning Wang;Thuong Nguyen Canh;Thong Bach;Qing Wang;Xiaopeng Sun;Haoyu Ma;Shijie Zhao;Junlin Li;Liangbin Xie;Shuwei Shi;Yujiu Yang;Xintao Wang;Jinjin Gu;Chao Dong;Xiaodi Shi;Chunmei Nian;Dong Jiang;Jucai Lin;Zhihuai Xie;Mao Ye;Dengyan Luo;Liuhan Peng;Shengjie Chen;Xin Liu;Qian Wang;Xin Liu;Boyang Liang;Hang Dong;Yuhao Huang;Kai Chen;Xingbei Guo;Yujing Sun;Huilei Wu;Pengxu Wei;Yulin Huang;Junying Chen;Ik Hyun Lee;Sunder Ali Khowaja;Jiseok Yoon
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Cited by: Papers (22)
This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the super-resolution and qua...Show More
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
Ren Yang;Radu Timofte;Meisong Zheng;Qunliang Xing;Minglang Qiao;Mai Xu;Lai Jiang;Huaida Liu;Ying Chen;Youcheng Ben;Xiao Zhou;Chen Fu;Pei Cheng;Gang Yu;Junyi Li;Renlong Wu;Zhilu Zhang;Wei Shang;Zhengyao Lv;Yunjin Chen;Mingcai Zhou;Dongwei Ren;Kai Zhang;Wangmeng Zuo;Pavel Ostyakov;Vyal Dmitry;Shakarim Soltanayev;Chervontsev Sergey;Zhussip Magauiya;Xueyi Zou;Youliang Yan;Pablo Navarrete Michelini;Yunhua Lu;Diankai Zhang;Shaoli Liu;Si Gao;Biao Wu;Chengjian Zheng;Xiaofeng Zhang;Kaidi Lu;Ning Wang;Thuong Nguyen Canh;Thong Bach;Qing Wang;Xiaopeng Sun;Haoyu Ma;Shijie Zhao;Junlin Li;Liangbin Xie;Shuwei Shi;Yujiu Yang;Xintao Wang;Jinjin Gu;Chao Dong;Xiaodi Shi;Chunmei Nian;Dong Jiang;Jucai Lin;Zhihuai Xie;Mao Ye;Dengyan Luo;Liuhan Peng;Shengjie Chen;Xin Liu;Qian Wang;Xin Liu;Boyang Liang;Hang Dong;Yuhao Huang;Kai Chen;Xingbei Guo;Yujing Sun;Huilei Wu;Pengxu Wei;Yulin Huang;Junying Chen;Ik Hyun Lee;Sunder Ali Khowaja;Jiseok Yoon
Year: 2022 | Conference Paper |
Multi-Scale Deep Compressive Imaging
Recently, deep learning-based compressive imaging (DCI) has surpassed conventional compressive imaging in reconstruction quality and running speed. While multi-scale sampling has shown superior performance over single-scale, research in DCI has been limited to single-scale sampling. Despite training with single-scale images, DCI tends to favor low-frequency components similar to conventional multi...Show More
Simplified Level Decision in Rate-distortion Optimized Quanitzation
2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
Rate-distortion optimized quantization (RDOQ) provides a considerable compression improvement in High efficiency video coding (HEVC) but with high computational complexity. This paper addresses a simplified RDOQ scheme for HEVC encoder by developing a level estimation (LE) bypass scheme based on the coefficient group location and transform coefficient value. Early termination of last nonzero coeff...Show More
A Frame Loss Concealment Solution for Spatial Scalable HEVC using Base Layer Motion
Thuc Nguyen Huu;Thuong Nguyen Canh;Xiem HoangVan;Byeungwoo Jeon
2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
Scalable High Efficiency Video Coding (SHVC) is the most recent video coding solution designed mainly for network adaptive or device adaptive applications. It follows a layered coding structure with one base layer (BL) and one or several enhancement layers (ELs) which can be unequally protected. SHVC is often sensitive to packet loss in unreliable networks, especially in case of ELs. In this paper...Show More
Focal Stack Based Light Field Coding for Refocusing Applications
Vinh Van Duong;Thuong Nguyen Canh;Thuc Nguyen Huu;Byeungwoo Jeon
2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)
Year: 2019 | Conference Paper |
Cited by: Papers (11)
Since a light field camera generates huge volume of image data, it requires powerful compression for efficient transmission and storage of its data. In its refocusing applications, users want to see their objects of interest at different depth levels. To address this, we first render a focal stack that contains multi-focus images, then compress it instead of original light field data. By doing so,...Show More
Performance Evaluation of AV1 Intra Coding Tools
2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)
Year: 2019 | Conference Paper |
Cited by: Papers (4)
To meet the growing requirement of servicing high-quality video contents, Alliance for Open Media (AOMedia) has recently released an open-source video coding scheme named as AOMedia Video Coding 1 (AV1). In this paper, we review its intra coding tools in details and evaluate their individual performance in On-Off tests. Our experimental results show that the additional directional mode and the rec...Show More
Difference of Convolution for Deep Compressive Sensing
2019 IEEE International Conference on Image Processing (ICIP)
Year: 2019 | Conference Paper |
Cited by: Papers (11)
Deep learning-based compressive sensing (DCS) has improved the single scale compressive sensing (CS) with fast and high reconstruction quality. Researchers have further extended it to multi-scale DCS which improves reconstruction quality based on Wavelet decomposition. In this work, we mimic the Difference of Gaussian via convolution and propose a scheme named as Difference of Convolution-based mu...Show More
Multi-Scale Deep Compressive Sensing Network
2018 IEEE Visual Communications and Image Processing (VCIP)
Year: 2018 | Conference Paper |
Cited by: Papers (15)
With joint learning of sampling and recovery, the deep learning-based compressive sensing (DCS) has shown significant improvement in performance and running time reduction. Its reconstructed image, however, losses high-frequency content especially at low subrates. This happens similarly in the multi-scale sampling scheme which also samples more low-frequency components. In this paper, we propose a...Show More
Simplified Rate-Distortion Optimized Quantization for HEVC
2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)
Year: 2018 | Conference Paper |
Cited by: Papers (5)
Rate-distortion optimized quantization (RDOQ) is shown to provide significant coding gain in HEVC but it also suffers from huge computational complexity. In this paper, we simplify the RDOQ tool in HEVC by enforcing more zero levels at high frequency locations without sacrificing coding efficiency much. First, we analyze the properties of each quantized level at different frequency locations. Subs...Show More
Decomposition-based multiscale compressed sensing
2018 International Workshop on Advanced Image Technology (IWAIT)
Year: 2018 | Conference Paper |
Cited by: Papers (3)
This paper proposes a generalized decomposition-based measurement matrix (DMM) to capture multi-scale compressive measurements. In the proposed method, we linearly decompose the sensing image to various scales, and then adaptively sample it at each scale to capture essential information. The restricted isometric property of DMM is proven for structured sparse and compressible signals with various ...Show More
Color Image Denoising via Cross-Channel Texture Transferring
IEEE Signal Processing Letters
Cited by: Papers (5)
Image denoising can reduce the perturbation inevitably generated during image signal acquisition and its subsequent processing. While the utilization of nonlocal properties can enhance the performance of the state-of-the-art denoising methods, a heavy computational burden is incurred especially for color images. Inspired by the high correlation in the texture information over color channels, for a...Show More
Multi-scale/multi-resolution Kronecker compressive imaging
2015 IEEE International Conference on Image Processing (ICIP)
Year: 2015 | Conference Paper |
Cited by: Papers (10)
As a universal sampling procedure, compressive sensing (CS) considers that all samples of compressible signal are equally important. However, it is not true in in image/video signal since human visual system is more sensitive to low frequency components. Therefore, CS theory has been extended to hybrid and multi-scale CS to better capture the low-frequency samples. The computational complexity is ...Show More
Compressive sensing of video with weighted sensing and measurement allocation
2015 IEEE International Conference on Image Processing (ICIP)
Year: 2015 | Conference Paper |
Cited by: Papers (2)
This paper proposes a compressive sensing of video (CSV) framework that utilizes statistical properties of video signal. The proposed scheme periodically acquires deterministic measurements (i.e., low frequency DCT coefficients) for key frames to improve recovering both key and nonkey frames. In addition, based on temporal correlation among frames, side information is generated for nonkey frames t...Show More
Detail-preserving compressive sensing recovery based on cartoon texture image decomposition
2014 IEEE International Conference on Image Processing (ICIP)
Year: 2014 | Conference Paper |
Cited by: Papers (10)
In this paper, we propose a detail-preserving reconstruction method for total variation-based recovery in low subrate compressive sensing using cartoon texture image decomposition and residual reconstruction. It iteratively decomposes and reconstructs cartoon and texture image components separately. A nonlocal structure-preserving filter is utilized to reduce staircase artifacts while preserving n...Show More
Edge-preserving nonlocal weighting scheme for total variation based compressive sensing recovery
2014 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2014 | Conference Paper |
Cited by: Papers (14)
Although total variation minimization technique is being widely used in compressive sensing recovery, it still suffers from the so called staircase artifact which is caused by losing fine details of image. As a solution for the problem, in this paper, we propose an edge-preserving weighting scheme utilizing nonlocal structure and histogram of natural image in the gradient domain. Experimental resu...Show More
Filter-aided recovery for block-based compressive sensing of images
Phuong Minh Pham;Khanh Quoc Dinh;Thuong Nguyen Canh;Byeungwoo Jeon
The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014)
Year: 2014 | Conference Paper |
This paper proposes a recovery algorithm for block-based compressive sensing of images, which iteratively performs a compressive sensing recovery and a denoising processes. The compressive sensing recovery is performed by MH-BCS-SPL, whereas the denoising is by BM3D. Experimental results show the superiority of the proposed recovery algorithm.Show More
Recovery algorithm based on total variation (TV) has shown its capability to recover high quality image in compressive sensing by preserving edges well but not fine details and textures. Recently, to improve this deficiency, characteristics of natural images are further utilized by adding some regularization terms into its recovery problem. In these efforts, this paper proposes a new regularizatio...Show More
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