Contributions to interframe coding

• URL: http://arxiv.org/abs/2203.16934v1
• Date: Thu, 31 Mar 2022 10:36:25 GMT
• Title: Contributions to interframe coding
• Authors: Marcos Faundez-Zanuy, Francesc Vallverdu-Bayes, Francesc Tarres-Ruiz
• Abstract summary: We propose a new approach to reduce the number of vectors, using different block sizes as a function of the local characteristics of the image. A second algorithm is proposed for an inter/intraframe coder.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Advanced motion models (4 or 6 parameters) are needed for a good representation of the motion experimented by the different objects contained in a sequence of images. If the image is split in very small blocks, then an accurate description of complex movements can be achieved with only 2 parameters. This alternative implies a large set of vectors per image. We propose a new approach to reduce the number of vectors, using different block sizes as a function of the local characteristics of the image, without increasing the error accepted with the smallest blocks. A second algorithm is proposed for an inter/intraframe coder.

Related papers

• In-2-4D: Inbetweening from Two Single-View Images to 4D Generation [54.62824686338408]
  We propose a new problem, In-between2-4D, for generative 4D (i.e., 3D + motion) in Splating from a minimalistic input setting. Given two images representing the start and end states of an object in motion, our goal is to generate and reconstruct the motion in 4D.
  arXiv  Detail & Related papers  (2025-04-11T09:01:09Z)
• Accelerated Sub-Image Search For Variable-Size Patches Identification Based On Virtual Time Series Transformation And Segmentation [0.0]
  This paper addresses two tasks: (i) fixed-size objects such as hay bales to be identified in an aerial image for a given reference image of the object, and (ii) variable-size patches such as areas on fields requiring spot spraying or other handling are to be identified in an image for a given small-scale reference image. The exact number of similar sub-images is not known a priori.
  arXiv  Detail & Related papers  (2024-10-20T15:43:50Z)
• Optimize and Reduce: A Top-Down Approach for Image Vectorization [12.998637003026273]
  We propose Optimize & Reduce (O&R), a top-down approach to vectorization that is both fast and domain-agnostic. O&R aims to attain a compact representation of input images by iteratively optimizing B'ezier curve parameters. We demonstrate that our method is domain agnostic and outperforms existing works in both reconstruction and perceptual quality for a fixed number of shapes.
  arXiv  Detail & Related papers  (2023-12-18T16:41:03Z)
• Video Frame Interpolation with Many-to-many Splatting and Spatial Selective Refinement [83.60486465697318]
  We propose a fully differentiable Many-to-Many (M2M) splatting framework to interpolate frames efficiently. For each input frame pair, M2M has a minuscule computational overhead when interpolating an arbitrary number of in-between frames. We extend an M2M++ framework by introducing a flexible Spatial Selective Refinement component, which allows for trading computational efficiency for quality and vice versa.
  arXiv  Detail & Related papers  (2023-10-29T09:09:32Z)
• Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution [90.16462805389943]
  We develop a spatially-adaptive feature modulation (SAFM) mechanism upon a vision transformer (ViT)-like block. Proposed method is $3times$ smaller than state-of-the-art efficient SR methods.
  arXiv  Detail & Related papers  (2023-02-27T14:19:31Z)
• Lightweight Image Codec via Multi-Grid Multi-Block-Size Vector Quantization (MGBVQ) [37.36588620264085]
  We present a new method to remove pixel correlations. By decomposing correlations into long- and short-range correlations, we represent long-range correlations in coarser grids. We show that short-range correlations can be effectively coded by a suite of vector quantizers.
  arXiv  Detail & Related papers  (2022-09-25T04:14:26Z)
• Efficient Motion Modelling with Variable-sized blocks from Hierarchical Cuboidal Partitioning [24.100530697346155]
  Motion modelling with block-based architecture has been widely used in video coding where a frame is divided into fixed-sized blocks that are motion compensated independently. We have investigated the potential of cuboids in motion modelling against the fixed-sized blocks used in scalable video coding.
  arXiv  Detail & Related papers  (2022-08-28T04:13:58Z)
• Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolution [61.95533972380704]
  Local implicit image function (LIIF) denotes images as a continuous function where pixel values are expansion by using the corresponding coordinates as inputs. LIIF can be adopted for arbitrary-scale image super-resolution tasks, resulting in a single effective and efficient model for various up-scaling factors. We propose a novel adaptive local image function (A-LIIF) to alleviate this problem.
  arXiv  Detail & Related papers  (2022-08-07T11:23:23Z)
• Many-to-many Splatting for Efficient Video Frame Interpolation [80.10804399840927]
  Motion-based video frame relies on optical flow to warp pixels from inputs to desired instant. Many-to-Many (M2M) splatting framework to interpolate frames efficiently. M2M has minuscule computational overhead when interpolating arbitrary number of in-between frames.
  arXiv  Detail & Related papers  (2022-04-07T15:29:42Z)
• BoundarySqueeze: Image Segmentation as Boundary Squeezing [104.43159799559464]
  We propose a novel method for fine-grained high-quality image segmentation of both objects and scenes. Inspired by dilation and erosion from morphological image processing techniques, we treat the pixel level segmentation problems as squeezing object boundary. Our method yields large gains on COCO, Cityscapes, for both instance and semantic segmentation and outperforms previous state-of-the-art PointRend in both accuracy and speed under the same setting.
  arXiv  Detail & Related papers  (2021-05-25T04:58:51Z)
• Powers of layers for image-to-image translation [60.5529622990682]
  We propose a simple architecture to address unpaired image-to-image translation tasks. We start from an image autoencoder architecture with fixed weights. For each task we learn a residual block operating in the latent space, which is iteratively called until the target domain is reached.
  arXiv  Detail & Related papers  (2020-08-13T09:02:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.