Classifying videos into distinct categories, such as Sport and Music Video, is crucial for multimedia understanding and retrieval. Traditional methods require video decompression to extract pixel-level features like color, texture, and motion, thereby increasing computational and storage demands. We introduce a novel direction for video classification that does not rely on pixel domain information. Instead, we use the sequence of video frame sizes extracted from compressed bitstreams as input for a ResNet-based deep neural network, without the need for bitstream decoding or parsing. This approach leverages information captured by modern video compression algorithms, particularly advanced spatial and temporal prediction methods found in modern video coding standards such as H.264/AVC, H.265.HEVC and H.266/VVC.

Conventionally, federated learning aims to optimize a single objective, typically the utility. However, for a federated learning system to be trustworthy, it needs to simultaneously satisfy multiple objectives, such as maximizing model performance, minimizing privacy leakage and training costs, and being robust to malicious attacks. Multi-Objective Optimization (MOO) aiming to optimize multiple conflicting objectives simultaneously is quite suitable for solving the optimization problem of Trustworthy Federated Learning (TFL). In this article, we unify MOO and TFL by formulating the problem of constrained multi-objective federated learning (CMOFL). Under this formulation, existing MOO algorithms can be adapted to TFL straightforwardly. Different from existing CMOFL algorithms focusing on utility, efficiency, fairness, and robustness, we consider optimizing privacy leakage along with utility loss and training cost, the three primary objectives of a TFL system. We develop two improved CMOFL algorithms based on NSGA-II and PSL, respectively, to effectively and efficiently find Pareto optimal solutions and provide theoretical analysis on their convergence. We design quantitative measurements of privacy leakage, utility loss, and training cost for three privacy protection mechanisms: Randomization, BatchCrypt (an efficient homomorphic encryption), and Sparsification. Empirical experiments conducted under the three protection mechanisms demonstrate the effectiveness of our proposed algorithms.

Due to differences in frame structure, existing multi-rate video encoding algorithms cannot be directly adapted to encoders utilizing special reference frames such as AV1 without introducing substantial rate-distortion loss. To tackle this problem, we propose a novel bayesian block structure inference model inspired by a modification to an HEVC-based algorithm. It estimates the posterior probabilistic distributions of block partitioning, and adapts early terminations in the RDO procedure accordingly. Experimental results show that the proposed method provides flexibility for controlling the tradeoff between speed and coding efficiency, and can achieve an average time saving of 36.1% (up to 50.6%) with negligible bitrate cost.

Compared with conventional image and video, light field images introduce the weight channel, as well as the visual consistency of rendered view, information that has to be taken into account when compressing the pseudo-temporal-sequence (PTS) created from light field images. In this paper, we propose a novel frame level bit allocation framework for PTS coding. A joint model that measures weighted distortion and visual consistency, combined with an iterative encoding system, yields the optimal bit allocation for each frame by solving a convex optimization problem. Experimental results show that the proposed framework is effective in producing desired distortion distribution based on weights, and achieves up to 24.7% BD-rate reduction comparing to the default rate control algorithm.

The High Efficiency Video Coding (HEVC) standard achieves 50% improvement incompression efficiency over the widely used H.264/AVC standard at a cost of much higher complexity. The increase in complexity is due to, among other factors, the time needed to findthe optimal partition structure among the more flexible possibilities for the coding units (CUs) and prediction units (PUs). Many classification based algorithms have been proposed to reduce this partition decision time, but the features that can be acquired from current HEVC encoding order may not be sufficient to control the loss in coding efficiency. In this paper, we proposed an Early-Split (ES) order for HEVC encoding, where the encoder checks the split mode before the non-square PU partition modes and utilizes the encoding output of the subCUs to expedite subsequent encoding. Experiments show that the proposed algorithm achieved an average of 48% saving in encoding time with only 0.92% loss in the coding performance.

In this paper, we first analyze impact of the distribution of sparse signals on reconstruction quality in compressive sensing through experimental results and heuristic analysis. We suggest that trinary/binary sparse signals are one of the most difficult signals to reconstruct in terms of error bounds. We then show that by incorporating linear or non-linear mapping prior to sensing, significant improvement in the recovery performance can be achieved.

We describe an image coding algorithm combining the DCT and noiselet information. The algorithm first transmits DCT information sufficient to reproduce a "low-quality" version of the image at the decoder. This image is then used both at the decoder and encoder to create a mutually known list of locations of likely significant noiselet coefficients. The coefficient values themselves are then transmitted to the decoder differentially, by subtracting, at the encoder, the low-quality image from the original image, obtaining the noiselet values and subjecting them to quantization and entropy coding. There remain significant opportunities for further work combining CS-inspired information theoretic techniques with the rate-distortion considerations that are critical in practical image communications.

Summary form only given. This paper has described an improved algorithm for reconstructing sparse binary signals using compressive sensing. The algorithm is based on the reweighted lqnorm optimization algorithm, but with the important additional operation of bounding in each round of the interior-point method iteration, and progressive reduction of q. Experimental results confirm that the algorithm performs well both in terms of the ability to recover an input signal as well as in terms of speed. We also found that both the progressive reduction and the bounding are integral to the improvement in performance. Future work includes extending this approach to Gaussian distributed, as opposed to binary inputs.