Compressed Sensing in Resource-Constrained Environments: From Sensing Mechanism Design to Recovery Algorithms

Compressed Sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It is promising that CS can be utilized in environments where the signal acquisition process is extremely difficult or costly, e.g., a resource-constrained environment like the smartphone platform, or a band-limited environment like visual sensor network (VSNs). There are several challenges to perform sensing due to the characteristic of these platforms, including, for example, needing active user involvement, computational and storage limitations and lower transmission capabilities. This dissertation focuses on the study of CS in resource-constrained environments. First, we try to solve the problem on how to design sensing mechanisms that could better adapt to the resource-limited smartphone platform. We propose the compressed phone sensing (CPS) framework where two challenging issues are studied, the energy drainage issue due to continuous sensing which may impede the normal functionality of the smartphones and the requirement of active user inputs for data collection that may place a high burden on the user. Second, we propose a CS reconstruction algorithm to be used in VSNs for recovery of frames/images. An efficient algorithm, NonLocal Douglas-Rachford (NLDR), is developed. NLDR takes advantage of self-similarity in images using nonlocal means (NL) filtering. We further formulate the nonlocal estimation as the low-rank matrix approximation problem and solve the constrained optimization problem using Douglas-Rachford splitting method. Third, we extend the NLDR algorithm to surveillance video processing in VSNs and propose recursive Low-rank and Sparse estimation through Douglas-Rachford splitting (rLSDR) method for recovery of the video frame into a low-rank background component and sparse component that corresponds to the moving object. The spatial and temporal low-rank features of the video frame, e.g., the nonlocal similar patches within the single video frame and the low-rank background component residing in multiple frames, are successfully exploited

Content Adaptive NN-Based In-Loop Filter for VVC

The most recent video coding standard VVC contains five in-loop filters to reduce compression artifacts that come from the common drawbacks of block-based hybrid compression framework. However, those traditional in-loop filters are insufficient to deal with the complicated compression artifacts. The emergence of Neural Networks (NNs) has brought significant advancements in the realm of image and video processing, offering a promising avenue for improving video compression. Many prior studies in this domain have focused on training models on large datasets to achieve generalization, rather than catering to specific content characteristics. In this work, we introduced a content-adaptive in-loop filter for Versatile Video Coding (VVC) working with other in-loop filters. The content adaptation is achieved by over-fitting a pre-trained model at the encoder side on the test data. To reduce the bitrate overhead, the Neural Network Compression and Representation (NNR) standard has been introduced which focuses on compressing NNs efficiently. Furthermore, rather than over-fitting all parameters within the NN model, we introduce a set of learnable parameters known as multipliers, which serve to further reduce the bitrate overhead. The proposed model takes auxiliary information including Boundary Strength (BS) and Quantization parameter (QP) as input. Additionally, we have conducted a comprehensive series of experiments to identify the optimal combination of hyperparameters for this approach. The results indicate coding gains of -2.07% (Y), -5.54% (Cb), -1.95% (Cr) Bjøntegaard Delta rate (BD-rate) for Class B and -1.34% (Y), -1.88% (Cb), -0.52% (Cr) Bjøntegaard Delta rate (BD-rate) for Class D with respect to the Peak Signal-to-Noise Ration (PSNR) on top of the Versatile Video Coding (VVC) Test Model (VVC) 12.0 with NN-based Video Coding (NNVC) 5.0, in Random Access (RA) configuration