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arXiv 2019年

作者： Shi, Jun Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

Rapid growing intelligent applications require optimized bit allocation in image/video coding to support specific task-driven scenarios such as detection, classification, segmentation, etc. Some learning-based frameworks have been proposed for this purpose due to their inherent end-to-end optimization mechanisms. However, it is still quite challenging to integrate these task-driven metrics seamlessly into traditional hybrid coding framework. To the best of our knowledge, this paper is the first work trying to solve this challenge based on reinforcement learning (RL) approach. Specifically, we formulate the bit allocation problem as a Markovian Decision Process (MDP) and train RL agents to automatically decide the quantization parameter (QP) of each coding tree unit (CTU) for HEVC intra coding, according to the task-driven semantic distortion metrics. This bit allocation scheme can maximize the semantic level fidelity of the task, such as classification accuracy, while minimizing the bit-rate. We also employ gradient class activation map (Grad-CAM) and Mask R-CNN tools to extract task-related importance maps to help the agents make decisions. Extensive experimental results demonstrate the superior performance of our approach by achieving 43.1% to 73.2% bit-rate saving over the anchor of HEVC under the equivalent task-related distortions. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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Deep Scalable Image Compression via Hierarchical Feature Decorrelation

Deep Scalable Image Compression via Hierarchical Feature Dec...

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Picture Coding Symposium, PCS

作者： Zongyu Guo Zhizheng Zhang Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

The following topics are dealt with: video coding; data compression; image coding; convolutional neural nets; decoding; learning (artificial intelligence); motion compensation; video codecs; image reconstruction; filt...

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No-Reference Light Field Image Quality Assessment Based on Micro-Lens Image

No-Reference Light Field Image Quality Assessment Based on M...

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Picture Coding Symposium, PCS

作者： Ziyuan Luo Wei Zhou Likun Shi Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

Light field image quality assessment (LF-IQA) plays a significant role due to its guidance to Light Field (LF) contents acquisition, processing and application. The LF can be represented as 4-D signal, and its quality depends on both angular consistency and spatial quality. However, few existing LF-IQA methods concentrate on effects caused by angular inconsistency. Especially, no-reference methods lack effective utilization of 2D angular information. In this paper, we focus on measuring the 2-D angular consistency for LF-IQA. The Micro-Lens Image (MLI) refers to the angular domain of the LF image, which can simultaneously record the angular information in both horizontal and vertical directions. Since the MLI contains 2D angular information, we propose a No-Reference Light Field image Quality assessment model based on MLI (LF-QMLI). Specifically, we first utilize Global Entropy Distribution (GED) and Uniform Local Binary Pattern descriptor (ULBP) to extract features from the MLI, and then pool them together to measure angular consistency. In addition, the information entropy of SubAperture Image (SAI) is adopted to measure spatial quality. Extensive experimental results show that LF-QMLI achieves the state-of-the-art performance.

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Deep Grammatical Multi-classifier for Continuous Sign Language Recognition

Deep Grammatical Multi-classifier for Continuous Sign Langua...

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IEEE International Conference on Multimedia Big Data (BigMM)

作者： Chengcheng Wei Wengang Zhou Junfu Pu Houqiang Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

In this paper, we propose a novel deep architecture with multiple classifiers for continuous sign language recognition. Representing the sign video with a 3D convolutional residual network and a bidirectional LSTM, we formulate continuous sign language recognition as a grammatical-rule-based classification problem. We first split a text sentence of sign language into isolated words and n-grams, where an n-gram is a sequence of consecutive n words in a sentence. Then, we propose a word-independent classifiers (WIC) module and an n-gram classifier (NGC) module to identify the words and n-grams in a sentence, respectively. A greedy decoding algorithm is employed to integrate words and n-grams into the sentence based on the confidence scores provided by both modules. Our method is evaluated on a Chinese continuous sign language recognition benchmark, and the experimental results demonstrate its effectiveness and superiority.

关键词： Videos Assistive technology Gesture recognition Feature extraction Task analysis Decoding Cats

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Improving Semantic Segmentation via Label Propagation and Temporal Consistency

Improving Semantic Segmentation via Label Propagation and Te...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Feiyu Qin Lumeng Cao Xuejin Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Semantic segmentation is a fundamental task in indoor scene understanding. Most previous supervised approaches rely on densely annotated image data sets. Due to the limited amount of images with segmentation labels, the performance of existing networks is greatly limited. In this paper, we exploit temporal correlation in video frames to improve the performance and robustness of segmentation networks. Two effective learning strategies are proposed to propagate the information from a few labeled frames to their immediate neighbor frames. First, we scale up training dataset for supervised semantic segmentation networks by generating pseudo ground-truth for neighboring frames from a labeled frame using filtered homography transformation. Furthermore, we introduce a self-supervised loss function to ensure temporal consistency between the segmentation results of adjacent frames. The experimental results demonstrate that our proposed method outperforms state-of-the-art techniques for semantic segmentation on NYU-Depth V2 dataset.

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Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports

Quality Assessment of Stereoscopic 360-degree Images from Mu...

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Picture Coding Symposium, PCS

作者： Jiahua Xu Ziyuan Luo Wei Zhou Wenyuan Zhang Zhibo Chen CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

Objective quality assessment of stereoscopic panoramic images becomes a challenging problem owing to the rapid growth of 360-degree contents. Different from traditional 2D image quality assessment (IQA), more complex aspects are involved in 3D omnidirectional IQA, especially unlimited field of view (FoV) and extra depth perception, which brings difficulty to evaluate the quality of experience (QoE) of 3D omnidirectional images. In this paper, we propose a multi-viewport based full-reference stereo 360 IQA model. Due to the freely changeable viewports when browsing in the head-mounted display, our proposed approach processes the image inside FoV rather than the projected one such as equirectangular projection (ERP). In addition, since overall QoE depends on both image quality and depth perception, we utilize the features estimated by the difference map between left and right views which can reflect disparity. The depth perception features along with binocular image qualities are employed to further predict the overall QoE of 3D 360 images. The experimental results on our public Stereoscopic OmnidirectionaL Image quality assessment Database (SOLID) show that the proposed method achieves a significant improvement over some well-known IQA metrics and can accurately reflect the overall QoE of perceived images.

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Robust Beamformer based on Magnitude Response Constraint and Sparse Constraint

Robust Beamformer based on Magnitude Response Constraint and...

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2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Lei, Songlin Qiu, Xiaolan DIng, Chibiao Zhang, Yueting Aerospace Information Research Institute Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS China Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728123455

Beamformer with magnitude response constraint can flexibly control the response region by specified beamwidth and response ripple, which has a significant performance against steering vector mismatch. However, a high sidelobe level of the beam is accompanied, resulting in performance degradation. To solve this problem, a novel robust beamformer based on magnitude response constraint and sparse constraint is proposed. This method adds the sparse constraint, that is, Lp-norm to the beamformer with magnitude response constraint, then the non-convex cost function can be formulated as a semidefinite programming (SDP) problem, finally the matrix decomposition theory is used to get the array weight vector. Simulation results demonstrate that the proposed method can not only produce large controlled region against steering vector mismatch and reduce the sidelobe level of the beampattern, but also achieve good performance in Signal to Interference plus Noise Ratio (SINR) enhancement. © 2019 IEEE.

关键词： Vectors

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Edge-Guided Panoramic Video Stitching with Limited Overlap

Edge-Guided Panoramic Video Stitching with Limited Overlap

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Chaoyu Xie Xuejin Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Video stitching remains a challenging problem in computer vision. In this paper, we propose a novel edge-guided method to stitch multiple videos that have small overlapped regions. Our algorithm consists of three steps: (1) spherical projection of the input video frames based on camera calibration, (2) edge detection and edge-guided feature matching for video registration, and (3) seam optimization to eliminate distortions and ghosts in the composited panoramic videos. The experimental results and user studies demonstrate that our method is robust to videos that have small overlapped regions and produces more visually pleasing panoramic videos than state-of-the-art techniques.

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An Energy Efficient Carry-Free Inner Product Unit

An Energy Efficient Carry-Free Inner Product Unit

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2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Yan, Wen Ercegovac, Milos D. Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China University of California Computer Science Department Los AngelesCA United States

ISBN: (纸本)9781728123455

An energy efficient truncated inner product unit is proposed in this paper. The proposed unit is pipelined and processes the m pairs of n-bit operands in serial, so that only one unit is required and it can be reused in each recurrence. Truncated inner product is produced so that no accumulation is needed for the lower part. The array reduction unit is bit-level pipelined to improve throughput and reduce the initial delay. A two-level [4:2] adder is proposed to accumulate redundant vectors with non-uniform input arrival. The proposed inner product is compared with other truncated inner product units using fully merged arithmetic, partially merged arithmetic and the counter based inner product for different precision and vector length. Our proposed inner product unit has significant reduction in area, power and energy. © 2019 IEEE.

关键词： Pipelines

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Video-based point cloud compression artifact removal

arXiv

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arXiv 2021年

作者： Akhtar, Anique Gao, Wen Li, Li Li, Zhu Jia, Wei Liu, Shan Department of Computer Science and Electrical Engineering University of Missouri-Kansas City Kansas CityMO64110 United States Tencent America 661 Bryant St. Palo AltoCA94301 United States Department of Computer Science and Electrical Engineering University of Missouri-Kansas City Kansas CityMO64110 United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

—Photo-realistic point cloud capture and transmission are the fundamental enablers for immersive visual communication. The coding process of dynamic point clouds, especially video-based point cloud compression (V-PCC) developed by the MPEG standardization group, is now delivering state-of-the-art performance in compression efficiency. V-PCC is based on the projection of the point cloud patches to 2D planes and encoding the sequence as 2D texture and geometry patch sequences. However, the resulting quantization errors from coding can introduce compression artifacts, which can be very unpleasant for the quality of experience (QoE). In this work, we developed a novel out-of-the-loop point cloud geometry artifact removal solution that can significantly improve reconstruction quality without additional bandwidth cost. Our novel framework consists of a point cloud sampling scheme, an artifact removal network, and an aggregation scheme. The point cloud sampling scheme employs a cube-based neighborhood patch extraction to divide the point cloud into patches. The geometry artifact removal network then processes these patches to obtain artifact-removed patches. The artifact-removed patches are then merged together using an aggregation scheme to obtain the final artifact-removed point cloud. We employ 3D deep convolutional feature learning for geometry artifact removal that jointly recovers both the quantization direction and the quantization noise level by exploiting projection and quantization prior. The simulation results demonstrate that the proposed method is highly effective and can considerably improve the quality of the reconstructed point cloud. Copyright © 2021, The Authors. All rights reserved.

关键词： geometry

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