In compressed sensing (CS) based CMOS image sensors (CS-CIS), the ternary measurement matrix determines the compression performance in terms of decoded image quality versus sampling rate (data rate). Several studies have been carried out to investigate the effect of Hadamard and Walsh projection order selection on image reconstruction quality by simply reordering orthogonal matrices [1]. However, there is still room for improvement in the quality of reconstructed images from these works, especially at low SR. In this paper, we propose a structured measurement matrix called Zigzag ordered Walsh matrix (ZoW), which outperforms at low sampling rates. Firstly, the Walsh matrix is divided into several measurement patterns. Because the lower frequency component in an image plays a more critical role in determining the image quality, we arrange low-frequency patterns on the upper-left corner, and the frequency increases according to the zigzag scan order. Then, vectorize each pattern and stacking back into ZoW matrix. Hence, under various sampling rates, the proposed ZoW always remains the lowest frequency patterns which are the most critical patterns. Comparing with the existing measurement matrices, recovery errors are improved by 4.09dB in PSNR on average and provided significantly better image quality via visual perception when sampling rates are 5%~15%.In Table 1, we can observe that the reconstruction quality via PSNR is improved by 4.63dB, and SSIM is improved 69% on average when the sampling rate is 10%.

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].

Summary form only given. Compressed sensing-based CMOS image sensor (CS-CIS) has gained significant interest in the past few years. It can greatly reduce on-chip processor complexity, transmission cost, and storage requirement compared to conventional sampling method using Nyquist-Shannon rate. CS-CIS performs acquisition and compression simultaneously and transfers all heavy computation burden components to decoder, where the measurement streams can be processed and analyzed with unlimited resources, resulting in a low-complexity encoder. Thus, it is very suitable for transmitting only applications, where computational resource and power is limited. However, spatial and temporal redundancy in measurement has become a primary concern which it is necessary to further compress. In this paper, we proposed temporal redundancy reduction in compressive video sensing by using moving detection and inter-coding. Firstly, the moving detection is performed coding area extraction using local adaptive threshold to classify the measurement with an association of error distinction. However, false-positive detection could be occurred randomly, which transmission cost can be increasing uncertainty. To reduce transmission costs, the adaptive quantization parameters are adjusted by how frequently the area is detected. Moreover, we further compress the detected area by encoding the difference of current measurement and the best-matched measurement in neighboring frames. Finally, an efficient recovery algorithm of sparse signal is performed by using -minimization via primal-dual interior-point algorithm and reconstructed by inverse fast Walsh-Hadamard transform with horizontal kernel filter to prevent staircase artifacts simultaneously. The experimental results show that our proposed can greatly reduce bandwidth usage in terms of BPP by 63.15%, improve in PSNR by 1.56dB, and SSIM by 14.81% on average when compared to the state-of-the-art works.

Compressive sensing (CS) is data acquiring and innovative mathematical approach that accelerate and efficient sampling from large into small volumes of data. Moreover, it could be dramatically reduced amounts of sensor, power consumption, storage size, and bandwidth which results in lower hardware costs [1]. In wireless cameras network for video surveillance, the large amount of data is produced. However, there is still a lot of redundant data in measurement domain. To solve this problem, coding techniques such as block-based CS (BCS), intra-prediction and quantization is applied to avoid higher rate-distortion than other CS frameworks. Therefore, new imaging architecture has been proposed to be sensed, removed redundant information, and compressed simultaneously, thus leading to the faster image acquisition system.