- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
A multi-order derivative feature-based quality assessment model for light field image
摘要： This paper presents an image quality assessment (IQA) model exploring the multi-order derivative feature, called Multi-order Derivative Feature-based Model (MDFM), for evaluating the perceptual quality of light field image (LFI). In our approach, for the input reference and distorted LFIs, the multi-order derivative features are extracted by using the discrete derivative filter to represent the image details in different degrees. Then, the similarities of the extracted derivative features are measured independently. Finally, the weight map is established through the maximum value of the second-order derivative feature of reference and distorted LFIs, which is further utilized to pool the similarity map for generating the final score. Extensive simulation results have demonstrated that the proposed MDFM is more consistent with the perception of the HVS on the evaluation of LFI than the classical and state-of-the-art IQA methods.
关键词： Multi-order derivative feature,Light field image,Image quality assessment
更新于2025-09-23 15:23:52
-
Handbook of Dynamic Data Driven Applications Systems || Light Field Image Compression
摘要： The light field image, also known as the plenoptic image, contains the information about not only the intensity of light in a scene but also the direction of the light rays in space. Since the light field image contains very rich photometric and geometric information, it will have very widespread application in the future. For example, immersive content capture for virtual and mixed reality presentation or depth from light field for auto driving applications. To be more specific, a light field image can be enhanced with physical models for an autonomous decision making process, which is also an important task of the Dynamic Data Driven Applications Systems (DDDAS). Besides, the rich geometry and photometric information contained in the light field can be updated with real-time measurements, which is a focus of DDDAS such as smart city related image and video processing tasks. However, to make the light field images easier to be utilized, one of the most important tasks is to compress the light field images efficiently so they can be easily distributed over the current communication infrastructure.
关键词： DDDAS,geometric,photometric,plenoptic image,compression,light field image
更新于2025-09-23 15:19:57
-
[IEEE 2018 - 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON) - Helsinki (2018.6.3-2018.6.5)] 2018 - 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON) - A NOVEL DISPARITY-ASSISTED BLOCK MATCHING-BASED APPROACH FOR SUPER-RESOLUTION OF LIGHT FIELD IMAGES
摘要： Currently, available plenoptic imaging technology has limited resolution. That makes it challenging to use this technology in applications, where sharpness is essential, such as film industry. Previous attempts aimed at enhancing the spatial resolution of plenoptic light field (LF) images were based on block and patch matching inherited from classical image super-resolution, where multiple views were considered as separate frames. By contrast to these approaches, a novel super-resolution technique is proposed in this paper with a focus on exploiting estimated disparity information to reduce the matching area in the super-resolution process. We estimate the disparity information from the interpolated LR view point images (VPs). We denote our method as light field block matching super-resolution. We additionally combine our novel super-resolution method with directionally adaptive image interpolation from [1] to preserve sharpness of the high-resolution images. We prove a steady gain in the PSNR and SSIM quality of the super-resolved images for the resolution enhancement factor 8x8 as compared to the recent approaches and also to our previous work [2].
关键词： Light Field Image Super-Resolution,Block Matching,4D Imaging,Resolution Enhancement
更新于2025-09-04 15:30:14