Conventional point cloud rendering methods are based on the assumption that the complete point cloud is used as input data. However, these methods exhibit limitations in real-world applications, particularly in-stream environments. In the real-world, the acquisition and transmission of point clouds inevitably involve compression, resulting in degraded point clouds that should be rendered effectively. To address these challenges, we proposed a novel point cloud rendering method through cylindrical projection. This approach projects the entire point cloud radially onto a 2D cylindrical map based on its central axis, enabling grid-based 2D processing. This method provides stable and consistent performance even with sparse or incomplete point clouds. Extensive benchmark experiments have revealed that the proposed framework effectively renders various scenes and objects without requiring additional fine-tuning and demonstrated superior performance and generalization ability in in-stream environments compared with conventional methods. The source code is available at https://github.com/crisp134/CRISP.

In this paper, we propose a voxel-based rendering framework specifically designed to improve the quality of compressed point clouds. Compressed point clouds often suffer from sparsity, uneven density, and geometric degradation, which lead to visual artifacts such as holes, noise, and the loss of fine structural details. To address these problems, our method converts geometry-based point cloud compression (G-PCC) data into a structured 3D voxel grid that preserves local geometric information while providing a consistent spatial representation for convolutional processing. This voxelization alleviates sparsity and density imbalance, enabling the network to effectively learn local and global geometric features without introducing holes. The framework integrates voxel-level structural reasoning with point-level radiance restoration through a hybrid pipeline that combines voxel networks and multilayer perceptrons. Structural voxel features are projected into 2D space and refined by a U-Net-based image network to restore high-frequency details and reduce compression artifacts. The training process employs a combined L2 and perceptual loss to balance structural fidelity and perceptual quality. Experiments conducted on the NeRFSynthetic dataset, converted into the G-PCC format, demonstrate that the proposed method achieves stable and accurate rendering even under sparse conditions. Compared with existing baselines such as BPCR, NeRF, and FreqPCR, our framework consistently achieves higher PSNR and SSIM values while yielding the lowest LPIPS, confirming its effectiveness for high-quality rendering of compressed point clouds. These results indicate that the proposed voxel-based approach provides a robust and efficient solution for immersive media and 3D reconstruction applications.

In recent times, digital image forensics is gaining increased attention in multimedia forensics owing to the widespread scam alertness. Several forensic methods have been studied to establish the integrity of digital images by disclosing manipulation fingerprints. Anti-forensic (AF) attacks on manipulated images, particularly deep learning-based adversarial attacks using generative adversarial network (GAN), have been successfully applied to delude forensic methods. Consequently, an efficacious, efficient, and robust counter-AF (CAF) method is required to secure the integrity of digital images. In this study, we propose a robust open-set multi-instance learning approach for exposing GAN-based AF on manipulated images by introducing additional GAN-based operations. First, we generate multiple real instances from real images using multiple additional generators. Then we train an embedding network collaboratively with multiple real instances in an open-set fashion. During training, the embedding network learns only real images and has no prior knowledge regarding AF images. In the testing phase, real and AF images are processed for detection. The proposed open-set CAF method can effectively detect AF images and is more robust against transferable updating.

Frame rate up-conversion (FRUC) is a technology for generating a higher frame rate video from one with a lower frame rate. The multi-view plus depth map (MVD) system has been recently considered a promising solution for three-dimensional video systems. Accordingly, an FRUC efficient solution for the MVD system is greatly needed. In this paper, we thus propose an FRUC approach that considers the MVD system characteristics. It divides a frame into multiple layers using a depth map, and it reconstructs occluded regions using neighboring views and depth map information. With reconstructed information, it accurately performs motion estimation when a region occlusion occurs. Experimental results demonstrated that the proposed method significantly improved the quality of interpolated frames, and it can effectively complement other conventional FRUC approaches.

This paper presents an efficient view synthesis distortion estimation method for 3-D video. It also introduces the application of this method to Advanced Video Coding (AVC)- and High Efficiency Video Coding (HEVC)-compatible 3-D video coding. Although the proposed view synthesis distortion scheme is generic, its use for actual 3-D video codec systems addresses the many issues caused by different video-coding formats and restrictions. The solutions for these issues are herein proposed. The simulation results show that the proposed scheme can achieve approximately 5.4% and 10.2% coding gains for AVC- and HEVC-compatible 3-D coding, respectively. In addition, the results show the remarkable complexity reduction of the scheme compared to the view synthesis optimization method currently used in 3-D-HEVC. The proposed method has been adopted into the presently developing AVC- and HEVC-compatible test model reference software.

Multiview video sequences with more than 3 color videos and additional supplementary data require huge amount of transmission and storage capacities. This paper proposes novel approaches to transfer such multiview video contents more efficiently by applying a mixed resolution video format and new compression schemes utilizing the characteristics of multiview video contents.

This paper presents an efficient intra prediction scheme for depth map coding in multi-view plus depth (MVD) system. As compared to the conventional intra prediction algorithm, the proposed method segments the current block into k regions, and applies different prediction scheme for each segmented region, which results in higher prediction accuracy by avoiding wrong estimation across different regions. Moreover, we also provide the efficient lossless coding scheme for region segmentation information based on unique depth characteristics, by which the proposed scheme is competitive w.r.t. R-D cost. The simulation results show the superior performance of the proposed scheme comparing to the H.264/AVC intra prediction and other edge-based intra scheme for depth map.

The denoising performance of the Non-Local Means (NLM) method decreases as the variance of additive white Gaussian noise becomes higher. In this paper, we explain this phenomenon and propose a modified version of the Non-Local Means (NLM) method, called the Enhanced-Weights NLM (EWNLM) algorithm, to denoise highly noisy images. The EWNLM algorithm evaluates weights from a pre-filtered image using the Gaussian kernel, which in turn result in more robust weight contributions from similar pixels in the search window. Experimental results are given to demonstrate the superior performance of the EWNLM scheme when the standard deviation of the additive white Gaussian noise (AWGN) is greater than 20.

The addition of a new component to the traditional synthesis-based texture video coding algorithm is investigated in this paper. That is, we add the side information in the form of low-quality video to enhance the texture video synthesis performance with reducing the unpleasant mismatch between analyzed and synthesized regions. As compared with the conventional synthesis algorithm, our algorithm is more flexible since the behavior and quality of the output texture can be adjusted by the amount of the side information, which is determined by the user. To this end, we develop an area-adaptive side information selection scheme that chooses the proper amount of the side information for a given bit budget. Furthermore, we propose a texture decomposition scheme that extracts the non-synthesizable illumination component from the source video for separate coding so as to maximize the synthesis functionality. The superior performance of the proposed texture video synthesis technique is demonstrated by several coding examples.

A speed up technique for the non-local means (NLM) image denoising algorithm based on probabilistic early termination (PET) is proposed. A significant amount of computation in the NLM scheme is dedicated to the distortion calculation between pixel neighborhoods. The proposed PET scheme adopts a probability model to achieve early termination. Specifically, the distortion computation can be terminated and the corresponding contributing pixel can be rejected earlier, if the expected distortion value is too high to be of significance in weighted averaging. Performance comparative with several fast NLM schemes is provided to demonstrate the effectiveness of the proposed algorithm.

A texture interpolation technique based on the locally piecewise auto-regressive (PAR) model and the non-local (NL) training procedure is investigated in this work. The proposed PAR/NL scheme selects model parameters adaptively based on local image properties with an objective to improve the interpolation performance of nonadaptive models, e.g., the bicubic algorithm. To determine model parameters for stochastic texture, we use the non-local (NL) learning algorithm to update and refine these local model parameters under the assumption that the PAR model parameters are self-regular. As compared to previous interpolation algorithms, the proposed PAR/NL scheme boosts texture details, and eliminates blurring artifacts perceptually. Experimental results are given to demonstrate the performance of the proposed technique.

A technique for film grain noise extraction, modeling and synthesis is studied and applied to high-definition video coding in this paper. Film grain noise enhances the natural appearance of pictures in high-definition video and should be preserved in coded video. However, the coding of video contents with film grain noise is expensive. In previous art, it was proposed to enhance the coding performance by extracting film grain noise from the input video at the encoder as a preprocessing step, and by resynthesizing and adding it back to the decoded video at the decoder as a postprocessing step. In a novel implementation of this approach, we first remove film grain noise from image/video with a variational denoising approach without distorting its original content. Then, we present a parametric model (consisting of a small set of parameters) to generate film grain noise that is close to the actual one in terms of a couple of observed statistical properties, such as the power spectral density and the crosschannel spectral correlation. Under this framework, the coding gain of denoised video is higher while the visual quality of the final reconstructed video is well preserved. Experimental results are provided to demonstrate the superior performance of the proposed approach.

This paper presents a synthesis-based texture coding technique that uses low-quality video as side information to control the output texture for video compression. As compared with the current pure synthesis algorithm, the proposed algorithm is generic, in the sense that the behavior and quality of the output texture can be adjusted by the amount of the side information and determined by the user. We develop an area-adaptive side information assignment technique to improve coding efficiency. Additionally, we present a fast-searching algorithm to reduce computational complexity. Simulations demonstrate the performance of the proposed technique.

A new technique for film grain noise extraction, modeling and synthesis is proposed and applied to the coding of high definition video in this work. The film grain noise is viewed as a part of artistic presentation by people in the movie industry. On one hand, since the film grain noise can boost the natural appearance of pictures in high definition video, it should be preserved in high-fidelity video processing systems. On the other hand, video coding with film grain noise is expensive. It is desirable to extract film grain noise from the input video as a pre-processing step at the encoder and re-synthesize the film grain noise and add it back to the decoded video as a post-processing step at the decoder. Under this framework, the coding gain of the denoised video is higher while the quality of the final reconstructed video can still be well preserved. Following this idea, we present a method to remove film grain noise from image/video without distorting its original content. Besides, we describe a parametric model containing a small set of parameters to represent the extracted film grain noise. The proposed model generates the film grain noise that is close to the real one in terms of power spectral density and cross-channel spectral correlation. Experimental results are shown to demonstrate the efficiency of the proposed scheme.