It takes a very long time to remove the noise generated in a large amount of multi-view. In particular, since the multi-view is photographed from multiple points or multiple cameras, the pattern of noise generated may be more diverse. This type of noise can arise in far more diverse patterns in multi-view images than in single-view images. Noise appears in various ways due to various causes, such as the amount of light reaching the camera sensor or reflecting off the dust in the air. During the image capturing step, noise can be generated for various reasons. Recently, light field-based virtual reality (VR) recognizes an interactive and immersive “walk-around space” by rendering an image of an arbitrary viewpoint through a combination of pixels. The light field information of dense multi-view images is frequently used in such applications as post-refocusing, super-resolution, occlusion reconstruction and depth estimation. Multi-view images or light field data, acquired from a multi-camera array by capturing a plurality of images of different viewpoints, are used in various applications, including the synthesis of a high-resolution image with a high dynamic range or the reconstruction of an occluded area through synthetic aperture photography (SAP). Experimental results show the proposed scheme significantly improves the compression efficiency of denoised views up to 76.05%, maintaining good denoising quality compared to the popular conventional denoise algorithms. Assuming the sequential processes of denoising and compression, multi-view geometry-based denoising is performed keeping the temporal correlation among views. In this paper, the structural characteristics of linear multi-view images are fully utilized to increase the denoising speed and performance as well as to improve the compression efficiency. Therefore, denoising is a prerequisite to produce multi-view-based image contents.

However, noise can be easily generated during image capturing, and these noisy images severely deteriorate both the quality of experience and the compression efficiency. The compression efficiency is also critical because a large amount of multi-view data needs to be stored and transferred. High image quality is essential in systems with a near-eye display device. Immersive virtual reality is an important example. The proposed method allows the fast importance sampling required by eye-path tracing algorithms in image synthesis.Multi-view or light field images have recently gained much attraction from academic and commercial fields to create breakthroughs that go beyond simple video-watching experiences. BTF synthesis from the model was implemented on a standard GPU, yielded interactive framerates. We carried out a psychophysical experiment comparing our method with LPCA method. These results compare well with several other BTF compression methods with predefined compression ratios, usually smaller than 1:200. The proposed model achieves low distortion and compression ratios 1:233–1:2040, depending on BTF sample variability. In the proposed model, luminance and chromaticity are treated separately to achieve further compression. BTF reconstruction from the model is then based on fast chained indexing into the nested stored code-books. The result of this algorithm is a set of index and scale code-books for individual dimensions. These functions are compressed in turn using a novel multi-level vector quantization algorithm. The model resamples input BTF data into a parametrization, allowing decomposition of individual view and illumination dependent texels into a set of multi-dimensional conditional probability density functions.

In this paper a novel BTF compression model is proposed. The Bidirectional Texture Function (BTF) is becoming widely used for accurate representation of real-world material appearance.

Vector light compressor