Existing low-light image enhancement methods generally suffer from high model complexity, considerable deployment cost, and semantic coupling between luminance and chrominance information, which often leads to fusion artifacts and restricts their application on resource-constrained devices. To address these issues, this paper proposes a lightweight low-light image enhancement network that combines multi-channel parallel attention and cross-fusion mechanisms. In the YCbCr color space, luminance and chrominance information are modeled separately. A lightweight denoising module and a multi-head self-attention mechanism are introduced to extract illumination structure features, while a multi-channel parallel attention module is introduced to enhance color-context modeling. In addition, a luminance-guided cross-fusion module is introduced to achieve collaborative enhancement of structure and color under joint channel-spatial attention optimization.

The input image is first converted into the YCbCr color space and decomposed into the Y, Cb, and Cr channels. The Y channel is processed by the DenoiseY module to suppress noise, and then passed through a multi-head self-attention module (MSA) to extract structural features. To improve contextual modeling efficiency, a pooling operation is inserted before MSA to reduce the number of tokens. Meanwhile, the Cb and Cr channels are enhanced by the MCPA module, which performs multi-scale attention modeling to extract richer color-context information.

Subsequently, in the LCA module, the denoised Y channel is used as a guidance feature to fuse with the Cb and Cr channels. This module incorporates channel attention based on the squeeze-and-excitation (SE) mechanism together with element-wise interaction, enabling luminance-guided structure-color fusion.

Finally, the fused features are restored to the RGB space through convolution layers to generate the enhanced image. By separately modeling luminance and chrominance information and introducing cross-guided fusion between the two branches, the proposed method effectively improves the overall quality of low-light images in terms of detail preservation, illumination enhancement, and color restoration.

The proposed network achieves 25.66 dB PSNR / 0.84 SSIM on LOLv1, 24.61 dB PSNR / 0.85 SSIM on LOLv2, and 20.26 dB PSNR / 0.57 SSIM on LSRW, outperforming recent methods such as Retinexformer. Meanwhile, the model contains only 0.059 million parameters and requires 10.06 GFLOPs, demonstrating its lightweight nature and computational efficiency. In addition, low-light face detection experiments conducted on the DARK FACE dataset show that all three detection metrics exceed 50%, indicating good generalization capability under low-light conditions.

Experimental results further show that MCPA is embedded into both the luminance and chrominance branches to extract contextual information at different scales and perform feature reweighting, thereby improving the network’s adaptive modeling ability for multi-scale structural patterns and color variations. The LCA module enables joint modeling of structure and color, where structural information provided by the Y branch dynamically guides the adjustment of chrominance features, effectively improving detail fidelity and color consistency in the enhanced images.

The proposed method achieves high-quality low-light image enhancement under resource-constrained conditions. Experimental results on multiple public datasets verify its superior performance, especially in achieving a favorable balance among structural fidelity, color naturalness, and computational efficiency. This study demonstrates that separate modeling of luminance and chrominance information, together with a luminance-guided cross-fusion strategy, can effectively alleviate the fusion artifacts caused by luminance-color coupling in conventional methods.

Immersive experience is a core determinant of user engagement and perceptual quality in metaverse-oriented virtual reality systems. Display characteristics play a central role in shaping immersion, as they directly regulate visual information delivery in near-eye environments and mediate the perceptual coupling between users and virtual scenes. In immersive perception research, multiple display dimensions have been incorporated into analytical frameworks, with field of view, resolution, and luminance commonly treated as fundamental parameters due to their direct influence on spatial coverage, visual clarity, and contrast perception. Color saturation, although closely associated with visual vividness, realism, and affective response, is frequently configured as a scene attribute rather than examined as an independent perceptual control variable. As a result, the quantitative relationship between saturation modulation and immersive experience remains insufficiently characterized, particularly under controlled display conditions. This gap limits the perceptually informed optimization of immersive display systems. The objective of this study was therefore to systematically investigate the effect of color saturation on immersive perception in virtual reality display systems and to develop an objective immersion quantification framework that integrates display parameters with neurophysiological responses.

A controlled experimental paradigm was designed based on a virtual reality near-eye display system to isolate the perceptual effects of color saturation while minimizing confounding visual factors. Six virtual scenes were constructed with identical spatial structure, interaction logic, object arrangement, environmental layout, and motion cues, thereby ensuring that any observed perceptual differences could be attributed primarily to saturation manipulation rather than scene content variation. Color saturation was manipulated at six predefined levels spanning low to high saturation conditions, while all other visual parameters, including luminance, contrast, resolution, and viewing geometry, were held constant throughout the experiment. Participants performed standardized interaction–perception tasks within each scene to maintain consistent cognitive demand, attentional allocation, and sensorimotor engagement across experimental conditions. During task execution, electroencephalogram (EEG) signals were continuously recorded using wearable devices, enabling real-time capture of neural activity associated with immersive perception under naturalistic interaction states. Following each task, participants reported their perceived immersion using structured subjective questionnaires administered immediately after exposure to reduce memory bias. This experimental protocol enabled synchronized acquisition of behavioral context, subjective evaluation, and physiological signals under systematically varied saturation conditions. Statistical analyses were conducted to assess the effects of color saturation on subjective immersion scores and EEG-based objective indicators. On this basis, representative EEG features were extracted and combined with saturation parameters to construct an objective immersion quantification model capable of producing a continuous immersion index across conditions, thereby linking display-level manipulation with perceptual and neurophysiological responses.

Data analysis indicated that color saturation exerted an extremely significant influence on subjective immersion scores (p < 0.001) and a highly significant influence on objective immersion evaluation results based on EEG measurements (p < 0.01). Further analysis revealed a clear enhancement effect within the medium-to-high saturation range. Immersion increased significantly as saturation rose from approximately 75% to full saturation, whereas lower saturation conditions were associated with reduced immersion stability and weaker neural engagement. These results suggest that color saturation functions as an effective immersion-enhancing factor when maintained within an appropriate perceptual range, likely by strengthening visual salience, scene vividness, and perceptual presence without inducing sensory overload or discomfort. Importantly, the observed trends were consistent across participants and scene configurations, indicating that the saturation–immersion relationship was not driven by individual preference alone but reflected a more general perceptual mechanism.To bridge subjective experience and physiological measurement, an objective immersion quantification model was constructed by integrating color saturation parameters with EEG-derived features. The predicted immersion scores generated by the model showed high consistency with subjective questionnaire ratings, yielding a correlation coefficient of r = 0.885. This strong correspondence indicates that the proposed model effectively captured essential variations in immersive experience across different saturation conditions. In addition to predictive accuracy, the model offers an interpretable mapping between display-level color modulation and neural response patterns, supporting its use as an analysis tool rather than a black-box predictor. The results further demonstrate the feasibility of employing lightweight EEG signals as a complementary tool for immersion assessment, reducing exclusive reliance on post-experience questionnaires and enabling more continuous and objective evaluation of user experience.

Color saturation is confirmed as a significant and quantifiable display parameter influencing immersive experience in virtual reality systems. Increasing saturation within an appropriate range enhances immersion at both subjective and neurophysiological levels, with particularly strong effects observed in medium-to-high saturation conditions. The integration of saturation parameters with EEG signals enables objective and continuous immersion evaluation, offering a practical pathway toward real-time immersion monitoring in interactive virtual environments. The proposed framework provides methodological support for isolating display-parameter effects and offers actionable guidance for immersive display design and content color tuning in metaverse applications. More broadly, the modeling approach establishes a foundation for adaptive display systems that dynamically adjust visual parameters in response to user perceptual states, thereby contributing to the development of perceptually optimized, user-centered virtual reality experiences.