Compression artifacts reduction by improved generative adversarial networks

Zhao, Zhihua; Sun, Qian; Yang, Haoran; Qiao, Heng; Wang, Zhigang; Wu, Dapeng

doi:10.1186/s13640-019-0465-0

Cited by 18 publications

(9 citation statements)

References 22 publications

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“…On the other hand, corner outliers, detection, and removal have been proposed by [15,25]. During compression, the corner outlier pixels are either considerable value or very small value pixels concerning surrounding pixels [8,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. Later on, Wang J. et al [37] presented an adaptive filter-based technique for compressed images of different regions.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient post-processing adaptive filtering technique to rectifying the flickering effects

et al. 2021

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Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.262, H.264, and MPEG-1 and MPEG-4, which itself produces lots of artifacts. This paper focuses on an efficient pre-and post-processing technique (PP-AFT) to address and rectify the problems of quantization error, ringing, blocking artifact, and flickering effect, which significantly degrade the visual quality of video frames. The PP-AFT method differentiates the blocked images or frames using activity function into different regions and developed adaptive filters as per the classified region. The designed process also introduces an adaptive flicker extraction and removal method and a 2-D filter to remove ringing effects in edge regions. The PP-AFT technique is implemented on various videos, and results are compared with different existing techniques using performance metrics like PSNR-B, MSSIM, and GBIM. Simulation results show significant improvement in the subjective quality of different video frames. The proposed method outperforms state-of-the-art de-blocking methods in terms of PSNR-B with average value lying between (0.7–1.9db) while (35.83–47.7%) reduced average GBIM keeping MSSIM values very close to the original sequence statistically 0.978.

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Section: Literature Reviewmentioning

confidence: 99%

An efficient post-processing adaptive filtering technique to rectifying the flickering effects

et al. 2021

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“…However, in view of the inaccuracy of tracking in the experiment, there is still room to advance in the tracking effect of strenuous moving targets in serious pose and contour variation condition. In recent years, some sophisticated algorithms have adopted detection algorithms [12,31] or deep learning algorithms [32][33][34], and future work can focus on those algorithms to further improve robustness and tracking accuracy.…”

Section: Discussionmentioning

confidence: 99%

Combining Spatio-Temporal Context and Kalman Filtering for Visual Tracking

Yang

Wang

et al. 2019

Mathematics

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As one of the core contents of intelligent monitoring, target tracking is the basis for video content analysis and processing. In visual tracking, due to occlusion, illumination changes, and pose and scale variation, handling such large appearance changes of the target object and the background over time remains the main challenge for robust target tracking. In this paper, we present a new robust algorithm (STC-KF) based on the spatio-temporal context and Kalman filtering. Our approach introduces a novel formulation to address the context information, which adopts the entire local information around the target, thereby preventing the remaining important context information related to the target from being lost by only using the rare key point information. The state of the object in the tracking process can be determined by the Euclidean distance of the image intensity in two consecutive frames. Then, the prediction value of the Kalman filter can be updated as the Kalman observation to the object position and marked on the next frame. The performance of the proposed STC-KF algorithm is evaluated and compared with the original STC algorithm. The experimental results using benchmark sequences imply that the proposed method outperforms the original STC algorithm under the conditions of heavy occlusion and large appearance changes.

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“…For learning‐based category, it is observed that RNN and non‐local NNs have not yet been investigated in spatial intrinsic artefacts reduction. Moreover, generative adversarial network (GAN) is recently used for compression artefacts reduction of colour image [161], but has not been exploited for compression artefacts reduction of depth maps. Using GANs for depth map enhancement might be beneficial especially when the registered colour image is used as a guide.…”

Section: Depth Map Artefacts Reductionmentioning

confidence: 99%