NASSBLiF: No-Reference Light Field Image Quality Assessment Via Neighborhood Attention and Scale Swin

Immersive media, such as Light Field images, enhance user experience by offering a high-fidelity spatial-angular representation of virtual environments. Challenges in transmitting and compressing content can cause noticeable artifacts, reducing quality. This highlights the need for efficient image quality assessment methods tailored to LF, given its large data requirements. Therefore, we introduce an efficient novel no-reference LF image quality assessment model utilizing neighborhood attention to extract spatial features and a Scale Swin Transformer to extract angular features. Due to their capability for extracting long-range dependencies and their linear computational cost relative to image size, both attention mechanisms prove highly effective in feature extraction for LF images. The proposed metric was evaluated on three datasets and outperformed state-of-the-art metrics.