Abstract: In order to objectively and efficiently measure, visualize and evaluate the quality of images in terms of sharpness, a method for determining a sharpness metric (S) of an image is described. The method includes performing edge detection that results in gradients for the pixels of the image, determining a value representing a contrast metric C of the image, calculating at least three different thresholds (t1,t2,t3) from the image content, and classifying the pixels according to their gradient into at least four groups defined by the thresholds. The sharpness metric (S) is calculated from relationships between the amounts of pixels in the different groups and a multiplicative factor (m) between the at least three thresholds.

Abstract: Television broadcast standards are continuously improved and new standards are introduced, often with increased resolution. Many users keep their television set that cannot take advantage of the improvements. However, in a case where a higher resolution video signal is available, additional services can be provided to the users of standard resolution television sets. A method for displaying a video comprises steps of enabling a user to define (71) first view settings, applying (72) the first view settings to a first view of a first video scene, determining (73) a first camera corresponding to the first view, storing (74) the first view settings, after a scene change towards a new scene detecting (76) that the new scene corresponds to the first view, and automatically applying (78) the first view settings for displaying the new scene.

Abstract: A noise-aware single-image super-resolution (SI-SR) method and apparatus automatically cancels additive noise while adding detail learned from lower scale of an input image. A recent and efficient in-place cross-scale self-similarity prior is exploited for both learning fine detail examples to complement the interpolation-based upscaled image patches and reducing image noise.

Abstract: An algorithm for performing super-resolution splits an input image or video into patches and relies on image self-similarity, wherein similar patches are searched in different downscaled versions of an image, using Approximate Nearest-Neighbor Fields (ANNF). The goal of ANNF is to locate with a minimal number of search iterations for each patch of a source image the k most similar patches in a downscaled version of the source image or video. A method for generating an ANNF for images of an input video comprises generating a plurality of downscaled versions of the images of the input video at different scales, generating an Inverse ANNF (IANNF) for the input video by finding for each patch of the downscaled images similar patches in the input video, generating an ANNF for the input video by reversing the IANNF, and filling gaps in the ANNF by random search.

Abstract: Known methods for generating super-resolved images from single input images have various disadvantages. An improved method for generating a super-resolved image from a single low-resolution input image comprises up-scaling the input image to generate an initial version of the super-resolved image, searching, for each patch of the low-resolution input image, similar low-resolution patches in first search windows within down-sampled versions of the input image, and determining, in less down-sampled versions of the input image, high-resolution patches that correspond to the similar low-resolution patches. The determined high-resolution patches are cropped, a second search window is determined in the initial version of the super-resolved image, and a best-matching position for each cropped high-resolution patch is searched within the second search window. Finally, each cropped high-resolution patch is added to the super-resolved image at its respective best-matching position.

Abstract: A method and an apparatus (20) for up-scaling an input image (12) are described, wherein a cross-scale self-similarity matching using superpixels is employed to obtain substitutes for missing details in an up-scaled image. The apparatus (20) comprises a superpixel vector generator (7) configured to generate (10) consistent superpixels for the input image (12) and one or more auxiliary input images (I1, I3) and to generate (11) superpixel test vectors based on the consistent superpixels. A matching block (5) performs a cross-scale self-similarity matching (12) across the input image (12) and the one or more auxiliary input images (I1, I3) using the superpixel test vectors. Finally, an output image generator (22) generates (13) an up-scaled output image (O2) using results of the cross-scale self-similarity matching (12).

Abstract: A noise-aware single-image super-resolution (SI-SR) method and apparatus automatically cancels additive noise while adding detail learnt from lower scale of an input image. In contrast to common SI-SR techniques, the input image is not necessarily assumed to be a clean source of examples. Instead, a recent and efficient in-place cross-scale self-similarity prior is exploited for both learning fine detail examples to complement the interpolation-based upscaled image patches and reducing image noise. Experimental results show a promising performance, despite of the relatively simple algorithm. Both objective and subjective evaluations show large quality improvements when upscaling images immersed in noise.

Abstract: In order to objectively and efficiently measure, visualize and evaluate the quality of images in terms of sharpness, a method for determining a sharpness metric (S) of an image is described. The method includes performing edge detection that results in gradients for the pixels of the image, determining a value representing a contrast metric C of the image, calculating at least three different thresholds (t1, t2,t3) from the image content, and classifying the pixels according to their gradient into at least four groups defined by the thresholds. The sharpness metric (S) is calculated from relationships between the amounts of pixels in the different groups and a multiplicative factor (m) between the at least three thresholds.

Abstract: A method for performing super-resolution on an input image having low resolution, comprises generating a generic training data set of descriptors extracted from regions of training images, and for each patch of the input image, determining a defined number of nearest neighbor regions, extracting example patches from the nearest neighbor regions and collecting the example patches in an example patch data base, determining a combination of low-resolution example patches that, according to their descriptors, optimally approximate the current patch, and constructing a high-resolution patch, wherein a super-resolved image is obtained.

Abstract: A method and an apparatus for improving a main image by fusing the richer information contained in a secondary image are described. A 3D structure of objects contained in the secondary image is retrieved and a parallax-corrected version of the secondary image is generated using the 3D structure. For this purpose a camera pose for which a projection of the 3D structure of the objects contained in the secondary image best resembles the perspective in the main image is determined and the parallax-corrected version of the secondary image is synthesized based on the determined camera pose. The parallax-corrected version of the secondary image is then fused with the main image.

Abstract: A method and an apparatus for deblurring an image from a sequence of images are described. The image comprises pixels. A motion estimation stage estimates motion of the pixels of the image. A point spread function modeling stage models point spread functions for the pixels of the image using the motion estimates. A deconvolution stage determines a deblurred estimate of the image using the modeled point spread functions. A feedback loop allows iteratively improving the motion estimates for the pixels until the modeled point spread functions converge.

Abstract: Television broadcast standards are continuously improved and new standards are introduced, often with increased resolution. Many users keep their television set that cannot take advantage of the improvements. However, in a case where a higher resolution video signal is available, additional services can be provided to the users of standard resolution television sets. A method for displaying a video comprises steps of enabling a user to define (71) first view settings, applying (72) the first view settings to a first view of a first video scene, determining (73) a first camera corresponding to the first view, storing (74) the first view settings, after a scene change towards a new scene detecting (76) that the new scene corresponds to the first view, and automatically applying (78) the first view settings for displaying the new scene.

Abstract: A method for performing super-resolution on an input image having low resolution, comprises generating a generic training data set of descriptors extracted from regions of training images, and for each patch of the input image, determining a defined number of nearest neighbor regions, extracting example patches from the nearest neighbor regions and collecting the example patches in an example patch data base, determining a combination of low-resolution example patches that, according to their descriptors, optimally approximate the current patch, and constructing a high-resolution patch, wherein a super-resolved image is obtained.

Abstract: Known methods for generating super-resolved images from single input images have various disadvantages. An improved method for generating a super-resolved image from a single low-resolution input image comprises up-scaling the input image to generate an initial version of the super-resolved image, searching, for each patch of the low-resolution input image, similar low-resolution patches in first search windows within down-sampled versions of the input image, and determining, in less down-sampled versions of the input image, high-resolution patches that correspond to the similar low-resolution patches. The determined high-resolution patches are cropped, a second search window is determined in the initial version of the super-resolved image, and a best-matching position for each cropped high-resolution patch is searched within the second search window. Finally, each cropped high-resolution patch is added to the super-resolved image at its respective best-matching position.

Abstract: A method and an input-output device are proposed for rendering content. Further, a servicing device is proposed for delivering pre-rendered content. For rendering, a portion of processing or memorizing resources of said input-output device is allocated such that remaining resources of said input-output device are sufficient for maintaining the input-output capability of the device. Then, it is determined that an amount of resources required for rendering exceeds the allocated resources and a corresponding degree of pre-rendering is determined, too. On the serving device, pre-rendering of the content according to the determined degree is performed and the pre-rendered content is delivered from the serving device to the input-output device. Since server-side rendering is limited to a required degree, bandwidth constraints can be met more easily. Furthermore, rendering occurs more distributed and can be adjusted dynamically, thus, response time can be reduced.

Abstract: A method for performing super-resolution comprises steps of generating the high-resolution, low-frequency spatial and temporal bands of the input video sequence by interpolation, synthesizing the high-resolution, high-frequency spatial band by cross-frame spatial high-frequency extrapolation, and fusing these two bands to generate the spatio-temporally super-resolved video sequence. A corresponding system for performing super-resolution comprises a stage where the high-resolution, low-frequency spatial and temporal bands of the input video sequence is generated by interpolation, a stage where the high-resolution, high-frequency spatial band is synthesized by cross-frame spatial high-frequency extrapolation, and a stage where these two bands are fused to generate the spatio-temporally super-resolved video sequence.

Abstract: The invention relates to the improvement of the resolution of regularly sampled multi-dimensional signals, where a single low-resolution signal is available. These methods are generically referred to as example-based super-resolution or single-image super-resolution. The method for super-resolving a single image comprises three stages. First, an interpolation-based up-scaling of the input image is performed, followed by an equivalent low-pass filtering operation on the low-resolution image. The second stage comprises a search for low-frequency matches between an inspected patch in the high-resolution image and patches in a local neighborhood in the low-resolution low-frequency image, including partly overlapping patches, and accumulating the high-frequency contribution obtained from the low-resolution image. The third stage comprises adding the contributions of the low-frequency band of the high-resolution image and the extrapolated high-frequency band.

Abstract: In a method for performing super-resolution of a single image, a high-resolution version of an observed image is generated by exploiting cross-scale self-similarity, wherein up-scaling and analysis filters are used. The up-scaling and analysis filters are adaptively selected according to local kernel cost.

Abstract: An algorithm for performing super-resolution splits an input image or video into patches and relies on image self-similarity, wherein similar patches are searched in different downscaled versions of an image, using Approximate Nearest-Neighbor Fields (ANNF). The goal of ANNF is to locate with a minimal number of search iterations for each patch of a source image the k most similar patches in a downscaled version of the source image or video. A method for generating an ANNF for images of an input video comprises generating a plurality of downscaled versions of the images of the input video at different scales, generating an Inverse ANNF (IANNF) for the input video by finding for each patch of the downscaled images similar patches in the input video, generating an ANNF for the input video by reversing the IANNF, and filling gaps in the ANNF by random search.

Abstract: The invention relates to an improved robustness in upscaling the resolution of regularly sampled multi-dimensional signals, where a single low-resolution signal is available. These methods are referred to as example-based super-resolution or single-image super-resolution. A method for super-resolving a single image comprises three stages. First, an interpolation-based up-scaling of the input image is performed, followed by a Local De-noising step in contour regions. The second stage comprises extrapolation through cross-scale block matching, wherein an extrapolated high-frequency band is obtained that is de-noised through regularization in the same contour regions. The third stage comprises adding the contributions of the low-frequency band of the high-resolution image and the extrapolated high-frequency band.