Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG
We present an end-to-end image compression system based on compressive sensing. The presented system integrates the conventional scheme of compressive sampling and reconstruction with quantization and entropy coding. The compression performance, in terms of decoded image quality versus data rate, is shown to be comparable with JPEG and significantly better at the low rate range. We study the parameters that influence the system performance, including (i) the choice of sensing matrix, (ii) the trade-off between quantization and compression ratio, and (iii) the reconstruction algorithms. We propose an effective method to jointly control the quantization step and compression ratio in order to achieve near optimal quality at any given bit rate. Furthermore, our proposed image compression system can be directly used in the compressive sensing camera, e.g. the single pixel camera, to construct a hardware compressive sampling system.
Code (0)
등록된 구현이 없습니다.
Tasks
Compressive SensingImage CompressionQuantizationSimilar Papers 제목 키워드 기반
Compressive Sensing via Low-Rank Gaussian Mixture Models
We develop a new compressive sensing (CS) inversion algorithm by utilizing the Gaussian mixture model (GMM). While the compressive sensing is performed globally on the entire image as implemented in our lensless camera, …
Compressive SensingImage Restoration from Patch-based Compressed Sensing Measurement
A series of methods have been proposed to reconstruct an image from compressively sensed random measurement, but most of them have high time complexity and are inappropriate for patch-based compressed sensing capture, be…
compressed sensingCompressive SensingImage ReconstructionImage RestorationLensless Compressive Imaging
We develop a lensless compressive imaging architecture, which consists of an aperture assembly and a single sensor, without using any lens. An anytime algorithm is proposed to reconstruct images from the compressive meas…
Compression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging
Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet and alike. They are the basic tool in image compression, in image restoration, i…
Compressive SensingImage CompressionImage ReconstructionImage RestorationJPEG Inspired Deep Learning
Although it is traditionally believed that lossy image compression, such as JPEG compression, has a negative impact on the performance of deep neural networks (DNNs), it is shown by recent works that well-crafted JPEG co…
Deep LearningFine-Grained Image Classificationimage-classificationImage Classification+2