Efficient Color Artifact Removal Algorithm Based on High-Efficiency Video Coding (HEVC) for High-Dynamic Range Video Sequences

Lee, Jong-Hyeok; Lee, Young-Woon; Jun, Dongsan; Kim, Byung-Gyu

doi:10.1109/access.2020.2984012

Cited by 12 publications

(4 citation statements)

References 30 publications

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“…Hu et al [16] proposed a singular valued decomposition (SVD) technique. In [30][31][32][33][34][35][36] Fields of experts (FoE) technique is used to remove blocking artifacts. The methods mentioned above are based on the principle of estimation theory which is again an iterative technique.…”

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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“…In terms of artifact reduction of compressed video sequences, CNN based video restoration methods show better performance than the conventional method. Lee et al have proposed an algorithm to remove color artifacts using block-level quantization parameter offset control in compressed High Dynamic Range (HDR) videos [28]. On the other hand, Dai et al have proposed CNN based video restoration, namely Variable-filter-size Residue-learning CNN (VRCNN) [14], which can be applied to compressed images by High Efficiency Video Coding (HEVC) [29].…”

Section: Related Workmentioning

confidence: 99%

Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images

Kim¹,

Jun²

2022

Computers, Materials &Amp; Continua

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Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications. In general, image compression can introduce undesired coding artifacts, such as blocking artifacts and ringing effects. In this paper, we proposed a Multi-Scale Feature Attention Network (MSFAN) with two essential parts, which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images. Multiscale feature extraction layers have four Feature Extraction (FE) blocks. Each FE block consists of five convolution layers and one CA block for weighted skip connection. In order to optimize the proposed network architectures, a variety of verification tests were conducted using validation dataset. We used Computer Vision Center-Clinic Database (CVC-ClinicDB) consisting of 612 colonoscopy medical images to evaluate the enhancement of image restoration. The proposed MSFAN can achieve improved PSNR gains as high as 0.25 and 0.24 dB on average compared to DnCNN and DCSC, respectively.

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“…In daily use, the quality of media images and photographic images can be assessed. In computer vision tasks, the quality of the generated images in image enhancement [1], image super-resolution [2], and image restoration tasks [3][4] can be assessed. In industrial applications such as detection and recognition tasks, high-quality images are screened through image quality assessment to improve the stability of the application system.…”

Section: Introductionmentioning

confidence: 99%

No-Reference Image Quality Assessment based on Quality Awareness Feature and Multi-task Training

Lai¹,

Chu²,

Leng³

2022

J Multimed Inf Syst

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The existing image quality assessment (IQA) datasets have a small number of samples. Some methods based on transfer learning or data augmentation cannot make good use of image quality-related features. A No Reference (NR)-IQA method based on multi-task training and quality awareness is proposed. First, single or multiple distortion types and levels are imposed on the original image, and different strategies are used to augment different types of distortion datasets. With the idea of weak supervision, we use the Full Reference (FR)-IQA methods to obtain the pseudo-score label of the generated image. Then, we combine the classification information of the distortion type, level, and the information of the image quality score. The ResNet50 network is trained in the pre-train stage on the augmented dataset to obtain more quality-aware pre-training weights. Finally, the fine-tuning stage training is performed on the target IQA dataset using the quality-aware weights to predicate the final prediction score. Various experiments designed on the synthetic distortions and authentic distortions datasets (LIVE, CSIQ, TID2013, LIVEC, KonIQ-10K) prove that the proposed method can utilize the image quality-related features better than the method using only single-task training. The extracted quality-aware features improve the accuracy of the model.

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Efficient Color Artifact Removal Algorithm Based on High-Efficiency Video Coding (HEVC) for High-Dynamic Range Video Sequences

Cited by 12 publications

References 30 publications

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

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

Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images

No-Reference Image Quality Assessment based on Quality Awareness Feature and Multi-task Training

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