Panoramic images are critical for immersive VR/AR and 6DoF yet degraded by compression artifacts, projection distortion, and uneven sampling, with existing hybrid CNN-Transformer models struggling to reconcile fine details and structural consistency in panoramas; to address this, we propose Dynamic Channel Collaboration (DCC-Former) for panoramic enhancement, inspired by primate vision's hierarchical processing and three strategies: strengthening local feature representation via reparameterization and gating, enhancing global context with adaptive self-attention, and enabling cross-scale aggregation through cascaded multi-scale fusion, aligned with biological vision's ventral-dorsal stream division and fovea-periphery resource allocation to balance detail preservation, global consistency, and computational efficiency-extensive experiments on benchmark datasets demonstrate DCC-Former outperforms SOTA in restoration quality and inference efficiency, providing a practical-efficient paradigm for high-resolution panoramic enhancement.

This work proposed a non-reference Just Recognized Distortion (JRD) prediction framework for object detection task based on Generative Adversarial Network (GAN) image generation. Inspired by the concept of Just Noticeable Difference (JND), the JRD is used to describe the threshold of image distortion acceptable for machine vision tasks. Centered around JRD, the proposed framework primarily consisted of a JRD image generation network and a residual-guided JRD regression network. Among them, the JRD image generation network was trained in conjunction with a multi-scale discriminator in the form of GAN. Additionally, we had compiled a dataset comprising over 130,000 images for the YOLOv7 object detection task, and it was used to validate the effectiveness of our proposed framework. Experiments indicated that the framework can approximate the JRD for object detection task with notable accuracy. Consequently, adopting our proposed framework for image compression could reduce the bitrate by 50% while maintaining high accuracy in task.

The diversity of video content and unpredictability of distortions in user-generated content (UGC) videos pose a challenge for video quality assessment (VQA). Most existing methods are difficult to model complete visual perception loop to accurately capture video content and predict perceived quality. Thus, as shown in Figure 1 , this paper proposes a bidirectional hierarchical semantic extraction structure for VQA (BHSE-VQA), which simulates visual feedforward and feedback perception processes. Firstly, the feedforward and feedback multi-level network (FFMNet) based on the reverse hierarchy theory is designed to extract and adjust hierarchical semantic features on the bidirectional pathway. Then, considering the different effects of feature depth on visual perception results, this paper suggests a multi-level weight redistribution (MWR) strategy that makes use of the attention characteristics of the feedback outputs to realign the weights at each stage of the feedforward outputs. Finally, through a temporal attention fusion network (TAFNet), this paper further extracts the detailed features arising from feedforward and feedback perceptual differences and obtains quality scores. The experimental results show that the proposed model exceeds 0.845 on both SRCC and PLCC on YouTube-UGC database.

An audio-video collaborative JND estimation model for multimedia data was proposed in this paper by delving into the impact of audio signals on HVS. Firstly, we extract and analyze the temporal perceptual features of audio in terms of loudness, duration, and audio energy. Secondly, the spatial feature of audio is analyzed using audio-guided visual saliency. Based on the temporal and spatial perceptual features of audio, an audio adjustment factor is designed and integrated with a video-based JND model to propose the audio-video collaborative JND estimation model. Detailed performance results of different JND models are shown in Table. 1 . The simulation results validate that the proposed JND model significantly improves the JND estimation accuracy of multimedia data with excellent performance and stronger distortion concealment ability, with strong competitiveness among the state-of-the-art models.

This work proposed a spatio-temporal JND model based on semantic attention. Firstly, the principal semantic features affecting visual attention are extracted, including the semantic sensitivity, objective area and shape, central bias and contextual complexity, and the HVS responses of these four features are explored and quantified. Secondly, the semantic attention model is constructed by inscribing the attentional competition model, considering the interaction between different objects with limited perception resources. Finally, the obtained semantic attention weighting factor is combined with the basic spatial attention model to develop an improved transform domain JND model. Detailed performance results of different JND models are shown in Tab. 1. The simulation results validate that the proposed JND profile is highly consistent with HVS, with strong competitiveness among the state-of-the-art models.