Standard test image

About: Standard test image is a research topic. Over the lifetime, 5217 publications have been published within this topic receiving 98486 citations.

Effective and Efficient Blind Quality Evaluator for Contrast Distorted Images

Abstract: This paper mainly focuses on developing a blind quality assessment method that can effectively and efficiently evaluate the quality of contrast distorted images without requiring reference information Through experiments, we discover and validate that the global intensity change is the main characteristic of contrast distorted images and has a close relationship to the perceptual quality With these observations, two elements are utilized to quantify this characteristic, ie, the maximum information entropy of intensity values and the Kullback–Leibler (K–L) divergence between the test image’s intensity histogram and the prior one based on the statistical experiment over a great number of high-quality images To be specific, the entropy represents the valuable information of an image and the K–L divergence reflects the change degree of intensity distribution In view of these, the proposed method is generated by combining these two elements linearly Extensive experiments on three publicly available databases demonstrate the superiority of the proposed method More specifically, it is more consistent with subjective evaluation results than the state-of-the-art image quality assessment methods and requires a lower computational complexity

Comparative analysis of image compression techniques

Abstract: Data compression as it is applicable to image processing is addressed. The relative effectiveness of several image compression strategies is analyzed. This study covers data compression algorithms, file format schemes, and fractal image compression. An overview of the popular LZW compression algorithm and its subsequent variations is also given. Several common image file formats are surveyed, highlighting the differing approaches to image compression. Fractal compression is examined in depth to reveal how an interactive approach to image compression is implemented. The performance of these techniques is compared for a variety of landscape images, considering such parameters as data reduction ratios and information loss.

Authentication method and authentication system

Abstract: An authentication method and an authentication system are provided. The authentication method includes the following steps. Providing a test image in a first state. Obtaining the test image in a second state in response to a rotating operation. Calculating a difference value between each of image hash values of the test image in the second state and the test image in a third state. Determining that an authentication is successful if the difference value is less than a threshold value, wherein the third state is a state in which the test image is up-right.

Laplacian Forests: Semantic Image Segmentation by Guided Bagging

Abstract: This paper presents a new, efficient and accurate technique for the semantic segmentation of medical images. The paper builds upon the successful random decision forests model and improves on it by modifying the way in which randomness is injected into the tree training process. The contribution of this paper is two-fold. First, we replace the conventional bagging procedure (the uniform sampling of training images) with a guided bagging approach, which exploits the inherent structure and organization of the training image set. This allows the creation of decision trees that are specialized to a specific sub-type of images in the training set. Second, the segmentation of a previously unseen image happens via selection and application of only the trees that are relevant to the given test image. Tree selection is done automatically, via the learned image embedding, with more precisely a Laplacian eigenmap. We, therefore, call the proposed approach Laplacian Forests. We validate Laplacian Forests on a dataset of 256, manually segmented 3D CT scans of patients showing high variability in scanning protocols, resolution, body shape and anomalies. Compared with conventional decision forests, Laplacian Forests yield both higher training efficiency, due to the local analysis of the training image space, as well as higher segmentation accuracy, due to the specialization of the forest to image sub-types.