Blind Stereoscopic Omnidirectional Image Quality Assessment Using Predictive Coding Hierarchy
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
3D Gaussian Splatting (3DGS) has emerged as an effective representation for novel view synthesis and 3D scene reconstruction, creating an increasing demand for reliable quality assessment. Unlike conventional image quality assessment (IQA), the quality of a 3DGS scene depends not only on the perceptual fidelity of rendered views, but also on scene-level factors such as spatial structure and cross-view consistency.
arXiv:2609.38689v1 Announce Type: new Abstract: Immersive displays can enable rich and diverse virtual experiences. Manually authoring every possible experience to realize this potential, however, is...
The paper introduces a 2D Gaussian Splatting pipeline that renders a dominant-eye RGB image and depth proxy, then reprojects and selectively patches the affiliated eye to reduce redundant work. By reusing alpha-blending weights and generating adaptive regions of interest, the method cuts sequential binocular rendering time by 15.5% to 28.8% and GPU memory by 6% to 11% on several datasets, with minimal quality loss. It demonstrates a practical efficiency‑quality trade‑off for static‑scene stereo rendering and suggests further evaluation on dynamic scenes and VR hardware.
The paper introduces a multidimensional observer model that represents images as distributions in a latent perceptual space and models human image quality judgment as comparisons of noisy samples. By aligning the model with neural representations in the primate ventral stream and fitting it to large-scale behavioral data, the authors demonstrate that the perceptual space required for human quality assessment is extremely low-dimensional relative to the image space. The study reveals that the structure of this perceptual space differs between low-level and high-level quality judgments, indicating that humans construct task-dependent perceptual spaces during visual decision making.
Low-light image enhancement algorithms (LIEAs) aim to improve the visibility of images captured under poor illumination. However, the enhancement process often introduces artifacts such as noise amplification, color shift, structural damage, and over-exposure, which degrade the perceptual quality of the enhanced images.
arXiv:2609.34367v2 Announce Type: replace Abstract: Learned image codecs (LICs) achieve high reconstruction quality, but their decoding speed is often insufficient for immersive virtual reality (VR)....