Scalable image coding based on epitomes
In this paper, we propose a novel scheme for scalable image coding based on the concept of epitome. An epitome can be seen as a factorized representation of an image. Focusing on spatial scalability, the enhancement layer of the proposed scheme contains only the epitome of the input image. The pixels of the enhancement layer not contained in the epitome are then restored using two approaches inspired from local learning-based super-resolution methods. In the first method, a locally linear embedding model is learned on base layer patches and then applied to the corresponding epitome patches to reconstruct the enhancement layer. The second approach learns linear mappings between pairs of co-located base layer and epitome patches. Experiments have shown that significant improvement of the rate-distortion performances can be achieved compared to an SHVC reference.
Code (0)
등록된 구현이 없습니다.
Tasks
Super-ResolutionSimilar Papers 제목 키워드 기반
Modeling Image Patches with a Generic Dictionary of Mini-Epitomes
The goal of this paper is to question the necessity of features like SIFT in categorical visual recognition tasks. As an alternative, we develop a generative model for the raw intensity of image patches and show that it …
ClassificationGeneral Classificationimage-classificationImage ClassificationMining self-similarity: Label super-resolution with epitomic representations
We show that simple patch-based models, such as epitomes, can have superior performance to the current state of the art in semantic segmentation and label super-resolution, which uses deep convolutional neural networks. …
Medical Image AnalysisSemantic SegmentationSuper-ResolutionCategory-Epitomes : Discriminatively Minimalist Representations for Object Categories
Freehand line sketches are an interesting and unique form of visual representation. Typically, such sketches are studied and utilized as an end product of the sketching process. However, we have found it instructive to s…
ObjectEPIM: Efficient Processing-In-Memory Accelerators based on Epitome
The utilization of large-scale neural networks on Processing-In-Memory (PIM) accelerators encounters challenges due to constrained on-chip memory capacity. To tackle this issue, current works explore model compression al…
Model CompressionNeural Architecture SearchQuantizationDeep Epitomic Convolutional Neural Networks
Deep convolutional neural networks have recently proven extremely competitive in challenging image recognition tasks. This paper proposes the epitomic convolution as a new building block for deep neural networks. An epit…
General Classificationimage-classificationImage Classification