The human visual system (HVS) is a hierarchical system, in which visual signals are processed hierarchically. In this paper, the HVS is modeled as a three-level communication system and visual perception is divided into three stages according to the hierarchical predictive coding theory. Then, a novel just noticeable distortion (JND) estimation scheme is proposed. In visual perception, the input signals are predicted constantly and spontaneously in each hierarchy, and neural response is evoked by the central residue and inhibited by surrounding residues. These two types’ residues are regarded as the positive and negative visual incentives which cause positive and negative perception effects, respectively. In neuroscience, the effect of incentive on observer is measured by the surprise of this incentive. Thus, we propose a surprise-based measurement method to measure both perception effects. Specifically, considering the biased competition of visual attention, we define the product of the residue self-information (i.e., surprise) and the competition biases as the perceptual surprise to measure the positive perception effect. As for the negative perception effect, it is measured by the average surprise (i.e., the local Shannon entropy). The JND threshold of each stage is estimated individually by considering both perception effects. The total JND threshold is finally obtained by non-linear superposition of three stage thresholds. Furthermore, the proposed JND estimation scheme is incorporated into the codec of Versatile Video Coding for image compression. Experimental results show that the proposed JND model outperforms the relevant existing ones, and over 16% of bit rate can be reduced without jeopardizing the perceptual quality.

With the proliferation of multi-modal data generated by various sensors, unsupervised multi-modal hashing retrieval has been extensively studied due to its advantages in storage, retrieval efficiency, and label independence. However, there are still two obstacles to existing unsupervised methods: (1) As existing methods cannot fully capture the complementary and co-occurrence information of multi-modal data, existing methods suffer from inaccurate similarity measures. (2) Existing methods suffer from unbalanced multi-modal learning and data semantic structure being corrupted in the process of hash codes binarization. To address these obstacles, we devise an effective CLIP-based Adaptive Graph Attention Network (CAGAN) for large-scale unsupervised multi-modal hashing retrieval. Firstly, we use the multi-modal model CLIP to extract fine-grained semantic features, mine similar information from different perspectives of multi-modal data and perform similarity fusion and enhancement. In addition, this paper proposes an adaptive graph attention network to assist the learning of hash codes, which uses an attention mechanism to learn adaptive graph similarity across modalities. It further aggregates the intrinsic neighborhood information of neighboring data nodes through a graph convolutional network to generate more discriminative hash codes. Finally, this paper employs an iterative approximate optimization strategy to mitigate the information loss in the binarization process. Extensive experiments on three benchmark datasets demonstrate that the proposed method significantly outperforms several representative hashing methods in unsupervised multi-modal retrieval tasks.

Due to the limited storage capacity and network bandwidth, an efficient rate control (RC) algorithm becomes more and more critical for multi-view video coding (MVC). Based on inter-view dependency and spatio-temporal correlation, a novel bit allocation method for multi-view texture video coding is proposed in this article. Firstly, considering that the distortion in the base view (BV) is directly transmitted to the dependent view (DV) by inter-view skip mode, a joint multi-view RD model is built based on the inter-view dependency. Based on the proposed joint multi-view RD model, a precise power model is derived to represent the target bitrates relationship between the BV and the DV. Secondly, since the P frame in the DV (P-DV) is mainly predicted from the corresponding I frame in the BV (I-BV) by disparity compensated prediction (DCP), the constant proportional relationship between the ratio of the average bitrates of the P-DV to the corresponding I-BV and the ratio of the total bitrates of the DV to the BV is discovered. Based on this discovery, a novel linear model is developed to assign the target bitrates of the P-DV. Finally, considering the spatio-temporal correlation, a new parameter prediction method is proposed for the R-$\lambda $ model in coding tree unit (CTU) level. Extensive experimental results show that the proposed overall method outperforms other state-of-the-art algorithms in terms of RD performance.