Neural Radiance Fields (NeRFs) demonstrate high efficiency in generating photo-realistic novel view. Recent studies introduce the trials on the 3D inpainting by NeRF. However, the performance of these works have been validated for data collected in a narrow range of multi-view, while degrade for the wide range of multi-view. To address this problem, we propose a novel NeRF framework to remove the obstacle and reproduce occluded areas in high quality for both wide and narrow range of multi-view. In this framework, we design a region coding network to carry out object segmentation. With the depth information, the segmentation component transfers a single obstacle mask to other views in high accuracy. By referring to the segmentation results, we introduce an innovative view selection mechanism to reconstruct the occluded area using supplementary information from multi-view and 2D inpainting. We also contribute to the evaluation of 3D scene de-occlusion by introducing a dataset including views captured in wide range and in pair with and without the obstacle object for comparison. We evaluate our framework in both narrow and wide range datasets by quantitative measurement and visually qualitative comparison, which confirm the competitive and superior performance of our framework.

High Dynamic Range (HDR) displays are being widely adopted in the consumer electronics market, pushing up the demand of HDR content. However, HDR contents converted from their Standard Dynamic Range (SDR) counterparts often suffer from the contour artifact phenomenon. To address this issue, we formulate a sensitive region aware weighted gradient minimization process to remove the contour artifact. In order to assign reasonable weights to the image gradients in the objective function, we use features composed of local smoothness and gradient directions to highlight the risk regions, and then propose an expansion ratio to precisely locate the artifact. Solving the objective function gives improved HDR images with the contour artifacts minimized and texture preserved. Experimental results demonstrate the effectiveness of our method by the measurement of the objective metrics and visual comparison.

The development of the display technology supports the application of High Dynamic Range (HDR) enabling devices. In order to meet the surging demand for the HDR media content, we propose a feature-transferred U-shaped network (FTUnet) to convert existing Standard Dynamic Range (SDR) images into their HDR counterparts. The proposed FTUnet is a feature transformation network that converts the encoded SDR features to the HDR features. This transformation network extracts features rich of spatial information by a self-attention mechanism, in order to improve the reconstruction of the over-exposed regions and avoid unreasonable patches. Besides, we propose an Excitation-Restoration (ER) sub-network to involve the inter-channel attention mechanism. The ER network is used to remove redundant information between channels and reserve the key features. Therefore, the proposed FTUnet can efficiently merge feature channels and contribute to the advantage in color accuracy for the generated HDR images. Experimental results show that our proposed FTUnet achieves state-of-the-art performance in both quantitative comparison and visual quality for the single HDR image reconstruction. The ablation study is also performed to demonstrate the effectiveness of each module of the proposed FTUnet.

We present a new counting-weighted total variation measure, which captures the consistency of gradient directions in local regions to distinguish edges and details. A novel optimization framework with the proposed counting-weighted total variation in the l1regularization term is then developed to realize the edge-preserving image smoothing. In order to solve the optimization problem, we adopt an iteratively re-weighted least square based algorithm. Experimental results demonstrate that the proposed method is capable of completing edge-preserving image smoothing, while avoiding blurring and over-sharpening the edge. The proposed method can also be used for the image detail enhancement without involving halos or gradient reversal artifacts, while achieve better quality scores in the comparison with other enhancement methods.

Emerging HDR videos enable the recording of adequate luminance information and representing realistic scenes to the audience. Due to the high precision of the HDR data recorded in the floating-point format, a quantization process is required to convert HDR data to integer data for compatibility with current transmission and display systems. In this study, a novel attention-aware quantization method is presented that attempts to preserve the contrast details in the region of interest of the human visual system. This method was applied in the context of HDR video coding. The proposed coding solution was compared with the current anchor solution in terms of the quality of the reconstructed video. Experimental results show that the proposed solution is able to improve the visual quality of encoded video with respect to the anchor solution. Additionally, the proposed solution achieves a bit-rate gain over the anchor with reference to the objective evaluation results.

In this paper, we propose an adaptive perceptual quantization method to convert the representation of high dynamic range (HDR) content from the floating point data type to integer, which is compatible with the current image/video coding and display systems. The proposed method considers the luminance distribution of the HDR content, as well as the detectable contrast threshold of the human visual system, in order to preserve more contrast information than the perceptual quantizer (PQ) in integer representation. Aiming to demonstrate the effectiveness of this quantizer for HDR video compression, we implemented it in a mapping function on the top of the HDR video coding system based on high efficiency video coding standard. Moreover, a comparison function is also introduced to decrease the additional bit-rate of side information, generated by the mapping function. Objective quality measurements and subjective tests have been conducted in order to evaluate the quality of the reconstructed HDR videos. Subjective test results have shown that the proposed method can improve, in a significant manner, the perceived quality of some reconstructed HDR videos. In the objective assessment, the proposed method achieves improvements over PQ in terms of the average bit-rate gain for metrics used in the measurement.

This paper presents a new adaptive perceptual quantization method for the High Dynamic Range (HDR) content. This method considers the luminance distribution of the HDR image as well as the Minimum Detectable Contrast (MDC) thresholds to preserve the contrast information during quantization. Base on this method, we develop a mapping function for HDR video compression and apply it to a HEVC Main 10 Profile-based video coding chain. Our experiments show that the proposed mapping function can efficiently improve the quality of the reconstructed HDR video in both objective and subjective assessments.

This paper presents a new scalable locally adaptive resolution lossless low-complexity (LAR-LLC) image codec. It is based on the LAR framework that is a multiresolution compression method supporting both lossy and lossless coding. To achieve an efficient low-complexity solution, each processing stage of the LAR is modified. For the first step, consisting of a pyramidal decomposition, a new reversible transform called hierarchical diagonal$S$transform (HD-ST) is proposed. The HD-ST operates on sets of data pairs, requiring only shift and add/sub operations. The second step performs the prediction of the transformed coefficients. The prediction scheme considers both inter- and intra-level information, and involves fixed weights. Then, a classification process is introduced to separate prediction errors into subclasses, using a context modeling approach. Finally, each subclass is coded by the Huffman coding algorithm. The results of the lossless compression experiments showed that LAR-LLC achieves the same compression performance as JPEG2000 with a lower complexity.

Along with the digital evolution, image post-production and indexing have become one of the most advanced and desired services in the lossless 3D image domain. The 3D context provides a significant gain in terms of semantics for scene representation. However, it also induces many drawbacks including monitoring visual degradation of compressed 3D image (especially upon edges), and increased complexity for scene representation. In this paper, we propose a semantic region representation and a scalable coding scheme. First, the semantic region representation scheme is based on a low resolution version of the 3D image. It provides the possibility to segment the image according to a desirable balance between 2D and depth. Second, the scalable coding scheme consists in selecting a number of regions as a Region of Interest (RoI), based on the region representation, in order to be refined at a higher bitrate. Experiments show that the proposed scheme provides a high coherence between texture, depth and regions and ensures an efficient solution to the problems of compression and scene representation in the 3D image domain.