Image conversion

About: Image conversion is a research topic. Over the lifetime, 2490 publications have been published within this topic receiving 19077 citations.

Color image recording apparatus

Abstract: A color image recording apparatus which can form by color correction, a high-quality image on a recording material, with which an image is printed on a back surface of a transparent layer thereof and the printed image is observed from the transparent layer side and can protect an image surface or improve image quality The apparatus comprises input means for inputting color image information; color correcting means for performing color correction of the color image information; converting means for performing mirror image conversion of the color image information; image forming means for performing image formation based on image signals from the color correcting means; and means for changing correcting parameters of the color correcting means and instructing a converting operation by the converting means in correspondence with types of recording materials of the image forming means

Apparatus and method for controlling variable bit rate in real time

Abstract: An apparatus and method for controlling a variable bit rate in real time are provided, including an encoding unit for performing a predetermined image conversion on present video data to be encoded and for encoding the video data into a variable bit rate by quantizing the conversion result, and a variable bit rate controller for allocating an overall bit estimate of the video data to be stored in a system to each frame according to a variation in video recording time, for detecting the complexity of frames based on a difference between pixels of the encoded video data and for determining a quantization factor using the detected complexity.

Hallucinating faces from thermal infrared images

Abstract: This paper addresses the face hallucination problem of converting thermal infrared face images into photo-realistic ones. It is a challenging task because the two modalities are of dramatical difference, which makes many developed linear models inapplicable. We propose a learning-based framework synthesizing the normal face from the infrared input. Compared to the previous work, we further exploit the local linearity in not only the image spatial domain but also the image manifolds. We have also developed a measurement of the variance between an input and its prediction, thus we can apply the Markov random field model to the predicted normal face to improve the hallucination result. Experimental results show the advantage of our algorithm over the existing methods. Our algorithm can be readily generalized to solve other multi-modal image conversion problems as well.

An improved contrast enhancing approach for color-to-grayscale mappings

Abstract: Despite the widespread availability of color sensors for image capture, the printing of documents and books are still primarily done in black-and-white for economic reasons. In this case, the included illustrations and photographs are printed in grayscale, with the potential loss of important information encoded in the chrominance channels of these images. We present an efficient contrast enhancement algorithm for color-to-grayscale image conversion that uses both luminance and chrominance information. Our algorithm is about three orders of magnitude faster than previous optimization-based methods, while providing some guarantees on important image properties. More specifically, our approach preserves gray values present in the color image, ensures global consistency, and locally enforces luminance consistency. Our algorithm is completely automatic, scales well with the number of pixels in the image, and can be efficiently implemented on modern GPUs. We also introduce an error metric for evaluating the quality of color-to-grayscale transformations.

Improved image conversion and encoding techniques

Abstract: A method of producing a depth map for use in the conversion of 2D images into stereoscopic images including the steps of: identifying at least one object within a 2D image; allocating the or each object with an identifying tag; allocating the or each object with a depth tag; and determining and defining an outline of each or the object.