Detecting Non-Aligned Double JPEG Compression Based on Amplitude-Angle Feature

Due to the popularity of JPEG format images in recent years, JPEG images will inevitably involve image editing operation. Thus, some tramped images will leave tracks of Non-aligned double JPEG ( NA-DJPEG ) compression. By detecting the presence of NA-DJPEG compression, one can verify whether a given JPEG image has been tampered with. However, only few methods can identify NA-DJPEG compressed images in the case that the primary quality factor is greater than the secondary quality factor. To address this challenging task, this article proposes a novel feature extraction scheme based optimized pixel difference ( OPD ), which is a new measure for blocking artifacts. Firstly, three color channels (RGB) of a reconstructed image generated by decompressing a given JPEG color image are mapped into spherical coordinates to calculate amplitude and two angles (azimuth and zenith). Then, 16 histograms of OPD along the horizontal and vertical directions are calculated in the amplitude and two angles, respectively. Finally, a set of features formed by arranging the bin values of these histograms is used for binary classification. Experiments demonstrate the effectiveness of the proposed method, and the results show that it significantly outperforms the existing typical methods in the mentioned task.

Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network

Since high quality realistic media are widely used in various computer vision applications, image compression is one of the essential technologies to enable real-time applications. Image compression generally causes undesired compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a densely cascading image restoration network (DCRN), which consists of an input layer, a densely cascading feature extractor, a channel attention block, and an output layer. The densely cascading feature extractor has three densely cascading (DC) blocks, and each DC block contains two convolutional layers, five dense layers, and a bottleneck layer. To optimize the proposed network architectures, we investigated the trade-off between quality enhancement and network complexity. Experimental results revealed that the proposed DCRN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed joint photographic experts group (JPEG) images compared to the previous methods.

Block-based compressed sensing of MR images using multi-rate deep learning approach

Deep learning (DL) models are highly research-oriented field in image compressive sensing in the recent studies. In compressive sensing theory, a signal is efficiently reconstructed from very small and limited number of measurements. Block-based compressive sensing is most promising and lenient compressive sensing (CS) approach mostly used to process large-sized videos and images: exploit low computational complexity and requires less memory. In block-based compressive sensing, a number of deep models are needed to train with each corresponding to different sampling rate. Compressive sensing performance is highly degraded through allocating low sampling rates to various blocks within same image or video frames. In this work, we proposed multi-rate method using deep neural networks for block-based compressive sensing of magnetic resonance images with performance that greatly outperforms existing state-of-the-art methods. The proposed approach is capable in smart allocation of exclusive sampling rate for each block within image, based on the image information and removing blocking artifacts in reconstructed MRI images. Each image block is separately sampled and reconstructed with different sampling rate and reassembled into a single image based on inter-correlation between blocks, to remove blocking artifacts. The proposed method surpasses the current state-of-the-arts in terms of reconstruction speed, reconstruction error, low computational complexity, and certain evaluation metrics such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and relative l2-norm error (RLNE).

An Efficient Lossless Compression algorithm for Medical images

Medical image compression plays a vital role in diagnosis of diseases which allowing manipulation, efficient, transmission and storage of color, binary and grayscale image. Before transmission and storage, a medical image may be required to be compressed. The objective of the study is to develop an efficient and effective technique for digital medical images which alleviates the blocking artifacts from grayscale image while retaining all relevant structures. In this paper, we demonstrate a highly engineered postprocessing filtering approach has been designed to remove blocking effects from medical images at low bit rate. The proposed technique is comprised of three strategies i.e. 1) a threshold valve scheme which is used to capture the pixel vectors containing blocking artifacts. 2) Blocking artifacts measurement techniques. The blocking artifacts are measured by three frequency related modes (low, Moderate and high frequency model). 3) A directional filter which is used to remove over-smoothing and ringing artifacts near edges of block boundary. The algorithm is tested on digital medical grayscale images from different modalities. The experimental results illustrate that the proposed technique is more efficient on the basis of PSNR-B, MSSIM, and MOS indices than the state-of-the-art methods. The proposed algorithm can be seamlessly applied in area of medical image compression which high transmission efficiency and acceptable image quality can be guaranteed.

3D DCT Based Image Compression Method for the Medical Endoscopic Application

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.