For bandwidth-constrained multimedia applications, simultaneously achieving ultra-low bitrate human video compression and accurate vertex prediction remains a critical challenge, as it demands the harmonization of dynamic motion modeling, detailed appearance synthesis, and geometric consistency. To address this challenge, we propose Sparse2Dense, a keypoint-driven generative framework that leverages extremely sparse 3D keypoints as compact transmitted symbols to enable ultra-low bitrate human video compression and precise human vertex prediction. The key innovation is the multi-task learning-based and keypointaware deep generative model, which could encode complex human motion via compact 3D keypoints and leverage these sparse keypoints to estimate dense motion for video synthesis with temporal coherence and realistic textures. Additionally, a vertex predictor is integrated to learn human vertex geometry through joint optimization with video generation, ensuring alignment between visual content and geometric structure. Extensive experiments demonstrate that the proposed Sparse2Dense framework achieves competitive compression performance for human video over traditional/generative video codecs, whilst enabling precise human vertex prediction for downstream geometry applications. As such, Sparse2Dense is expected to facilitate bandwidth-efficient human-centric media transmission, such as realtime motion analysis, virtual human animation, and immersive entertainment.

Distributed training improves the accuracy of large models by scaling parameter sizes. However, this scalability introduces communication bottlenecks between nodes. Gradient compression can alleviate these bottlenecks, but most existing methods are lossy, potentially compromising convergence and accuracy. This paper proposes a novel lossless compression algorithm for the floating-point gradient data, significantly improving compression efficiency compared to traditional lossless float compression algorithms while overcoming the shortcomings of lossy compression. First, new distance-run-length encoding and exponent prediction encoding using dynamic Huffman coding (D.Huff.) are designed based on the distinct statistical characteristics of zero and non-zero gradient data, effectively representing data values and zero locations. Second, an innovative hybrid Huffman coding (H.Huff.) is introduced, combining the advantages of dynamic and static Huffman coding. It enhances compression efficiency via local data adaptability, and simultaneously improves computational efficiency and hardware implementation friendliness with multiple fixed Huffman code tables. We also present a K-means-based probability vector clustering algorithm to effectively construct multiple prebuilt Huffman code tables. To evaluate the effectiveness of the proposed compression method, we selected two different deep neural network (DNN) learning tasks: (1) image classification, using ResNet50 and VGG16 for CIFAR-100 training, and ViT for ImageNet; and (2) language modeling, employing BERT on the IMDB dataset. The results are presented in Table 1. Furthermore, we tested mixed-precision training compression using the BF16 format. Experimental results demonstrate that our method achieves a lossless compression ratio of 0.4931 on typical gradient data, representing a significant 16.61% improvement over the state-of-the-art lossless float data compression approach, ndzip.

Asymmetric Numeral Systems (ANS) play a significant role in learned image codecs (LIC) because of their high coding efficiency. However, it constitutes a substantial portion of inference time, making it the main bottleneck in real-time LIC due to its high computational demands, complex control logic, and serial execution flow. To address these challenges, this paper introduces a hardware-oriented ANS algorithm hANS that reduces complex calculations for state encoding and state-symbol decoding. Furthermore, hANS employs fixed-latency calculation to eliminate control logic, which often causes inconsistent delays. To further enhance throughput, we propose a hardware architecture of ANS for LIC (ANS-LIC), introducing a novel hardware parallelism scheme that incorporates pipeline execution and multi-bin parallelism for encoding, along with multi-stream parallelism for decoding. Additionally, by optimizing the execution order, we achieve a reduction in hardware resource utilization during the decoding process. The proposed ANS-LIC hardware is implemented in RTL and synthesized using TSMC 65nm technology and the Alveo U250 Data Center Accelerator Card. We evaluate ANS-LIC on the Kodak and DIV2K LIC datasets, achieving a 1.17% compression ratio improvement over the SOTA method, Recoil. The implementation results and comparison with other works are presented in Table 1. The synthesis indicates that ANS-LIC requires only 385.5/393.0k gates for encoding and decoding, without SRAM. ANS-LIC achieves throughput improvements of 13.29×/1.47× for encoding and decoding over Recoil. In summary, the proposed ANS-LIC demonstrates substantial advantages.

By exploiting the potential of deep learning, video compressive sensing (CS) has achieved tremendous improvement recently. Due to the video CS is mainly served for the fixed scene in real life. In this paper, we propose a novel video compressive sensing with a low-complexity region-of-interest (ROI) detection method (VCSL). The ROI is located by calculating the difference between the reference frame and the following frames in our framework, which is compact without introducing any additional neural networks and parameters. Subsequently, only the detected ROIs are sampled and transmitted, except for the frame that is regarded as the background. The final re-constructed sequence would be attained by combining the ROIs and the background. Moreover, the proposed reference frame renewal method successfully solves the issue of background changing and achieves more accurate reconstructed results while reducing the sampling rate (SR) further. The specific testing results of VIRAT dataset are shown in Table. 1. We control the SR of our baseline method to be close to the other methods with fixed SR. As shown in Table. 1, our proposed VCSL achieves the best reconstruction performance among algorithms in the comparison while using the lowest average SR. Compared to the state-of-the-art counterparts, extensive experimental results have demonstrated that our proposed methods achieve superior performance while tackling more complex sequences and using a lower sampling rate. We believe that the proposed framework can be integrated into the other existing works to save the data of transmission.

Block-based compressive imaging enables new video acquisition methodology while reducing raw data size, theoretically eliminating the need for complex coding algorithms. However, the redundancy associated with random projection remains when transmitting raw data. This paper takes a fresh look at raw data structure by viewing it as cube made up of multiple downsampled images rather than a vector. As a result, each individual data point can be regarded as a pixel, allowing us to code with greater flexibility and versatility than current works. Following that, we propose a tailored video coding framework for this structure that includes directional 9 modes intra and inter prediction with block-matching motion estimation, transformation using DCT, and quantization with custom 4×4 quantization table as shown in Figure 1. We evaluated coding performance using various 4K datasets, resulting in 60-65% lower bit-per-pixels while maintaining visual quality compared to state-of-the-art works [1].