Current methodologies in distributed source coding have predominantly investigated decoder-focused strategies, emphasizing the alignment and exploitation of side information. This study introduces a paradigm shift by presenting an encoder-centric algorithm that conducts proactive optimization in the frequency domain. This shift is motivated by the current deep learning models' tendency to passively extract high-frequency elements, such as contours and content in the spatial domain at the encoder side, without considering the frequency characteristics of these spatial components. Unlike current trends, the proposed scheme actively selects the essential frequency components directly in the frequency domain by introducing an adaptive self-learning filter, enabling the encoder to discern and retain critical frequency components effectively and precisely. Furthermore, we align the side information in the spatial domain before feature extraction and implement an affine transformation-based alignment strategy to utilize the side information better. By leveraging the shared frequency domain components of the image pairs, the proposed algorithm adeptly learns affine coefficients to accomplish precise spatial alignment. This dual strategy of proactive encoder optimization and decoder alignment via affine transformations is highly efficient, outperforming existing state-of-the-art methods in distributed source coding when tested across two diverse datasets by an average of 0.5 dB in PSNR.

Deep network-based point cloud geometry compression is becoming more crucial and attractive due to constantly expanding 3D applications. The current strategy employing holistic point clouds as input imposes limitations on the compressed point cloud size and results in the loss of local geometry information, simplifying the repetition of local point cloud patterns. Nevertheless, employing point cloud patches as input results in a fresh set of erroneous points within the spliced point cloud. This occurrence can be attributed to the loss of global information and the relationship among the point cloud patches. Hence, this paper introduces a novel framework for progressive downsampling point cloud compression that follows the principles of two distinct methodologies. Our strategy still uses the autoencoder structure. Specifically, the encoder reduces the point cloud size and learns features incorporating local patch structure information and global semantic information. At the same time, the decoder upsamples the quantized and entropy-encoded features to reconstruct the original point cloud. More specifically, the geometrical details inside the patch and the relationship between patches are encoded to obtain the point cloud's global semantic and local geometry information. Concurrently, the computational burden is reduced by partitioning the complete point cloud input into segments and conducting continuous downsampling. Furthermore, we introduced an attention-based point cloud deconvolution module to address the localized repeating concentrations in reconstructed point clouds resulting from linear interpolation. This module samples the parent node and its neighboring relationships in the multi-space domain to improve the characteristics of the parent nodes to be sampled. It then uses the deconvolution to create sub-node characteristics that are more varied than the linear interpolation. Empirical evidence demonstrates that the proposed methodology achieves a effective equilibrium between the compression quality and ratio.