Over the past years, progressive methods have demonstrated promising performance in single image de-raining task. Nonetheless, current methods still struggle to precisely remove rain and preserve more image details during the progressive de-raining process, resulting in undesirable local artifacts or image detail loss. To tackle these limitations, a novel progressive approach, called Uncertainty-driven Progressive Single Image De-raining (UPSID), is proposed. Firstly, a powerful internal-and-external dense sub-network is devised, which effectively integrates three proper and flexible components, including dense connection, long short-term memory, and channel attention. Subsequently, the sub-network is further unfolded into multiple recurrent stages to form a progressive de-raining network. Finally, the overall progressive de-raining network is trained with an adaptive weighted loss to focus more on challenging pixels that characterize rain or texture/edge regions. Extensive quantitative and qualitative experiments confirm that the proposed UPSID outperforms multiple state-of-the-art algorithms, including single-stage, progressive, and uncertainty-driven single image de-raining methods. Additionally, this article also demonstrates the superiority of UPSID for other similar image restoration tasks such as single image de-snowing. The code will be publicly available at https://github.com/Lcai-QZ/UPSID .

Versatile Video Coding (VVC) inherits Screen Content Coding (SCC) tools such as Intra Block Copy (IBC) and Palette mode (PLT) from High Efficiency Video Coding Screen Content Coding (HEVC-SCC), which is known as VVC-SCC. VVC-SCC can effectively improve the efficiency of screen content encoding, but it can also lead to higher encoding complexity. In order to reduce the encoding complexity of VVC-SCC, we design a Hierarchical Feature Fusion Convolutional Neural Network (HFF-CNN) for predicting the current CU best intra prediction mode. The encoder determines the current CU intra prediction mode based on the network out put best prediction mode, angle intra prediction mode indexes, and adjacent CU mode probability, skipping unnecessary rate distortion cost calculations and speeding up the encoding process. Experimental results show that the proposed model reduces the intra frame encoding time of VCC-SCC by 36.6% while increasing the average BDBR by 0.44%. Compared to state-of-the-art algorithms, it exhibits a better balance between the rate distortion performance and the encoding complexity.

In this paper, a novel deep learning-based no-reference video quality assessment (NR-VQA) model for screen content videos (SCVs) is proposed, called the hierarchical spatiotemporal perceptual quality model (HSPQ). Firstly, the human visual system (HVS) perceives SCVs hierarchically, with varying sensitivity and attention to diverse attribute regions. Secondly, the visual redundancies are copious in the spatiotemporal domain of SCVs, degrading video quality to some extent. Based on these characteristics, the SCVs are decomposed into three hierarchical levels (i.e., patch level, frame level, and video level), which contain quality-related spatiotemporal information. Specifically, the visual saliency is first utilized for more salient textual and pictorial patches selection, and then, a dual-channel convolutional neural network integrating spatial-gate feature enhancement module (SGFEM) is designed to evaluate the quality of patches based on their attributes at the patch level separately. With spatial correlation, an adaptive blur-focused visual mechanism-based weighting strategy (BFWS) is proposed for converting quality scores from patch level to frame level. Finally, the video-level quality score, which reflects the temporal perceptual quality degradation, is combined to provide a comprehensive evaluation of distorted SCV quality. Experiments conducted on the Screen Content Video Database (SCVD) and Compressed Screen Content Video Quality (CSCVQ) databases demonstrate that our proposed HSPQ model aligns better with the visual perception of SCVs by the HVS. Moreover, it exhibits strong robustness compared to multiple classic and state-of-the-art image/video quality assessment models.

Screen content coding (SCC) in Versatile Video Coding (VVC) improves the coding efficiency of screen content videos (SCVs) significantly but results in high computational complexity due to the quad-tree plus multi-type tree (QTMT) structure of the coding unit (CU) partitioning. Therefore, we make the first attempt to reduce the encoding complexity from the perspective of CU partitioning for SCC in VVC. To this end, a fast CU partition prediction method is technically developed for VVC-SCC. First, to solve the problem of lacking sufficient SCC training data, SCVs are collected to establish a database containing CUs of various sizes and corresponding partition labels. Second, to determine the partition decision in advance, a novel WA-CNN model is proposed, which is capable of predicting two large CUs for VVC-SCC by adjusting the feature channels based on the size of input CU blocks. Finally, considering the imbalanced proportion of diverse partition decisions, a loss function with the weight that equalizes the contribution of imbalanced data is formulated to train the proposed WA-CNN model. Experimental results show that the proposed model reduces the SCC intra-encoding time by 35.65%${\sim }$38.31% with an average of 1.84%${\sim }$2.42% BDBR increase.

Compared with natural videos,screen content videos(SCVs) have particular features, such as fruitful sharper edges, lots of computer-generated graphics and texts, a large amount of flat areas. New tools are adopted toHEVC extensions on Screen Content Coding(HEVC-SCC), the traditional video rate control methods for natural videos are not effective for SCVs. For that, a3D-gradient guided rate control modelfor SCV coding, named 3DG-RC, is proposed to allocate bitrate more efficiently serving for SCVs. By considering the particular spatial-temporal characteristics of SCVs, the spatial and temporal feature extraction scheme is developed by using 3D-gradient filter and performed on the SCV to extract the spatial and temporal features simultaneously for guiding the bit allocation. The spatial-temporal feature similarity between three original reference SCV frames and their reconstructed ones is used to estimate the encoding parameters of the current block and frame. Experimental results demonstrate that compared with the classical and state-of-the-art rate control methods for HEVC-SCC, the proposed 3DG-RC algorithm achieves significant bitrate mismatch reduction and coding efficiency improvement for HEVC-SCC. In specific, the proposed 3DG-RC model outperforms the rate control model in SCM-8.8 with over 41.33% and 37.95% BD-BR savings on average, forlow delay B(LDB) andrandom access(RA) coding structure, respectively.

This paper proposes a new full-reference image quality assessment (IQA) model for performing perceptual quality evaluation on light field (LF) images, called the spatial and geometry feature-based model (SGFM). Considering that the LF image describe both spatial and geometry information of the scene, the spatial features are extracted over the sub-aperture images (SAIs) by using contourlet transform and then exploited to reflect the spatial quality degradation of the LF images, while the geometry features are extracted across the adjacent SAIs based on 3D-Gabor filter and then explored to describe the viewing consistency loss of the LF images. These schemes are motivated and designed based on the fact that the human eyes are more interested in the scale, direction, contour from the spatial perspective and viewing angle variations from the geometry perspective. These operations are applied to the reference and distorted LF images independently. The degree of similarity can be computed based on the above-measured quantities for jointly arriving at the final IQA score of the distorted LF image. Experimental results on three commonly-used LF IQA datasets show that the proposed SGFM is more in line with the quality assessment of the LF images perceived by the human visual system (HVS), compared with multiple classical and state-of-the-art IQA models.

This paper presents a new full-reference image quality assessment (IQA) method for conducting the perceptual quality evaluation of the light field (LF) images, called the symmetry and depth feature-based model (SDFM). Specifically, the radial symmetry transform is first employed on the luminance components of the reference and distorted LF images to extract their symmetry features for capturing the spatial quality of each view of an LF image. Second, the depth feature extraction scheme is designed to explore the geometry information inherited in an LF image for modeling its LF structural consistency across views. The similarity measurements are subsequently conducted on the comparison of their symmetry and depth features separately, which are further combined to achieve the quality score for the distorted LF image. Note that the proposed SDFM that explores the symmetry and depth features is conformable to the human vision system, which identifies the objects by sensing their structures and geometries. Extensive simulation results on the dense light fields dataset have clearly shown that the proposed SDFM outperforms multiple classical and recently developed IQA algorithms on quality evaluation of the LF images.

In this paper, we make the first attempt to study the subjective and objective quality assessment for the screen content videos (SCVs). For that, we construct the first large-scale video quality assessment (VQA) database specifically for the SCVs, called the screen content video database (SCVD). This SCVD provides 16 reference SCVs, 800 distorted SCVs, and their corresponding subjective scores, and it is made publicly available for research usage. The distorted SCVs are generated from each reference SCV with 10 distortion types and 5 degradation levels for each distortion type. Each distorted SCV is rated by at least 32 subjects in the subjective test. Furthermore, we propose the first full-reference VQA model for the SCVs, called the spatiotemporal Gabor feature tensor-based model (SGFTM), to objectively evaluate the perceptual quality of the distorted SCVs. This is motivated by the observation that 3D-Gabor filter can well stimulate the visual functions of the human visual system (HVS) on perceiving videos, being more sensitive to the edge and motion information that are often-encountered in the SCVs. Specifically, the proposed SGFTM exploits 3D-Gabor filter to individually extract the spatiotemporal Gabor feature tensors from the reference and distorted SCVs, followed by measuring their similarities and later combining them together through the developed spatiotemporal feature tensor pooling strategy to obtain the final SGFTM score. Experimental results on SCVD have shown that the proposed SGFTM yields a high consistency on the subjective perception of SCV quality and consistently outperforms multiple classical and state-of-the-art image/video quality assessment models.

In this paper, a novel full-referenceimage quality assessment(IQA) method for evaluating the quality of the distortedlight field(LF) image against its reference LF image is proposed, called thelog-Gabor feature-basedlight field coherence (LGF-LFC). Based on the fact that to compare two LF images, it essentially boils down to measure howcoherentof these two LF images, we attempt to measure the degree of their LFcoherence(LFC). To pursue this goal, the salient features from the reference and distorted LF images under comparison need to be extracted. By considering that the Gabor feature has the ability to well characterize thehuman visual system(HVS) perception, and the special characteristics of the LF images, themulti-scale andsingle-scale Gabor feature extraction schemes are developed to extract the multi-scale log-Gabor features from thesub-aperture images(SAIs) and the single-scale log-Gabor feature from theepi-polar images(EPIs), respectively. Note that the former can reflect the image details (via the SAIs), while the latter indicates the viewing consistency (via the EPI’s depth information). The similarity measurements are subsequently conducted on the comparison of their SAIs and that of their EPIs separately, followed by combining them together for arriving at the final score. Extensive simulation results have clearly demonstrated that the proposed LGF-LFC is more consistent with the perception of the HVS on the quality evaluation of the LF images than multiple classical and state-of-the-art IQA methods.

In this paper, an accurate and efficient full-reference image quality assessment (IQA) model using the extracted Gabor features, called Gabor feature-based model (GFM), is proposed for conducting objective evaluation of screen content images (SCIs). It is well-known that the Gabor filters are highly consistent with the response of the human visual system (HVS), and the HVS is highly sensitive to the edge information. Based on these facts, the imaginary part of the Gabor filter that has odd symmetry and yields edge detection is exploited to the luminance of the reference and distorted SCI for extracting their Gabor features, respectively. The local similarities of the extracted Gabor features and two chrominance components, recorded in the LMN color space, are then measured independently. Finally, the Gabor-feature pooling strategy is employed to combine these measurements and generate the final evaluation score. Experimental simulation results obtained from two large SCI databases have shown that the proposed GFM model not only yields a higher consistency with the human perception on the assessment of SCIs but also requires a lower computational complexity, compared with that of classical and state-of-the-art IQA models. The source code for the proposed GFM will be available at http://smartviplab.org/pubilcations/GFM.html.

In this paper, an accurate full-reference image quality assessment (IQA) model developed for assessing screen content images (SCIs), called the edge similarity (ESIM), is proposed. It is inspired by the fact that the human visual system (HVS) is highly sensitive to edges that are often encountered in SCIs; therefore, essential edge features are extracted and exploited for conducting IQA for the SCIs. The key novelty of the proposed ESIM lies in the extraction and use of three salient edge features-i.e., edge contrast, edge width, and edge direction. The first two attributes are simultaneously generated from the input SCI based on a parametric edge model, while the last one is derived directly from the input SCI. The extraction of these three features will be performed for the reference SCI and the distorted SCI, individually. The degree of similarity measured for each above-mentioned edge attribute is then computed independently, followed by combining them together using our proposed edge-width pooling strategy to generate the final ESIM score. To conduct the performance evaluation of our proposed ESIM model, a new and the largest SCI database (denoted as SCID) is established in our work and made to the public for download. Our database contains 1800 distorted SCIs that are generated from 40 reference SCIs. For each SCI, nine distortion types are investigated, and five degradation levels are produced for each distortion type. Extensive simulation results have clearly shown that the proposed ESIM model is more consistent with the perception of the HVS on the evaluation of distorted SCIs than the multiple state-of-the-art IQA methods.

The latest high efficiency video coding-based three dimensional video coding (3D-HEVC) exploits sophisticated intra prediction scheme to improve the coding performance of the depth video, but incurring heavy computational complexity. To address this problem, a low complexity depth intra coding method is presented for 3D-HEVC based on depth classification. Firstly, a database of depth prediction units (PUs) with three kinds of complexities is collected based on their optimal intra prediction mode. Then, the histogram of oriented gradient (HOG) features are extracted on these established database to train the classifier using support vector machine (SVM). For the current depth PU, the trained classifier is applied to determine its most possible complexity class so as to select the corresponding modes for involving the mode decision process. Experimental results show that the proposed method is able to significantly reduce the computational complexity while keeping almost the same coding performance of depth video and video quality of the synthesized view, compared with the exhaustive mode decision in 3D-HEVC.

The multiview video coding (MVC) adopts hierarchical B picture prediction structure and offers many prediction modes to effectively remove the spatial, temporal, and inter-view redundancies inherited in multiview video (MVV), but at the price of extremely high computational complexity. To address this problem, a fast MVC method by jointly using adaptive prediction structure (APS) and hierarchical mode decision (HMD) is proposed in this paper. The complexity reduction is achieved by: 1) designing four APSs for different MVV contents based on the fact that the contribution of the inter-view prediction varies from sequence to sequence and 2) developing an HMD scheme based on the observation that the relationship between the rate distortion (RD) cost and size of prediction mode is a unimodal function. In particular, for the current group of picture of the input MVV, the prediction structure is adaptively selected based on its characteristic, which is measured by the ratio of the average RD cost of the base view frames to the sum of the average RD cost of the base view frames and that of anchor frames in nonbase views, and then an HMD scheme is further performed to skip the checking process of those unlikely modes. The experimental results have shown that compared with the exhaustive mode decision in the MVC, the proposed algorithm achieves a reduction of the computational complexity by 83.49% on average, whereas incurring only a 0.086 dB loss in Bjontegaard delta peak signal-to-noise ratio and 2.97% increment on the total Bjontegaard delta bit rate.

A new multiple description coding approach is presented in this paper. In this approach, each significant wavelet coefficient is decomposed into two coefficients. One is made from the bits in the odd positions, whereas, the other is made from the bits in the even positions. These two types of coefficients are then grouped into two sub-signals. Two descriptions of coded data are formed from these sub-signals and transmitted over different channels. Similar to the polyphase transform and selective quantization multiple description coding algorithm (PTSQ), main descriptions and protecting data for the other description are separately coded. Since two sub-signals share the same hierarchical tree structure, only coefficient values in the redundancy parts need to be coded, and coding efficiency is improved. Experimental results have shown that the performance of the proposed scheme is better than that of PTSQ.