Rate-distortion-based scalable progressive image coding

1998 ◽  
Author(s):  
William K. Carey ◽  
Leslie A. Von Pischke ◽  
Sheila S. Hemami
Keyword(s):  
Image Coding ◽  
Rate Distortion

scholarly journals Rate Distortion Optimized Graph Partitioning for Omnidirectional Image Coding

2018 ◽  
Author(s):  
Mira Rizkallah ◽  
Francesca De Simone ◽  
Thomas Maugey ◽  
Christine Guillemot ◽  
Pascal Frossard
Keyword(s):  
Image Coding ◽  
Graph Partitioning ◽  
Rate Distortion ◽  
Omnidirectional Image

scholarly journals Improved Coefficient Recovery and Its Application for Rewritable Data Embedding

2021 ◽  
Vol 7 (11) ◽  
pp. 244
Author(s):  
Alan Sii ◽  
Simying Ong ◽  
KokSheik Wong
Keyword(s):  
Image Coding ◽  
Rate Distortion ◽  
Divide And Conquer ◽  
Storage Device ◽  
Data Embedding ◽  
Discrete Cosine Transformation ◽  
Recovery Method ◽  
Embedding Method ◽  
Coefficient Recovery ◽  
Recovery Methods

JPEG is the most commonly utilized image coding standard for storage and transmission purposes. It achieves a good rate–distortion trade-off, and it has been adopted by many, if not all, handheld devices. However, often information loss occurs due to transmission error or damage to the storage device. To address this problem, various coefficient recovery methods have been proposed in the past, including a divide-and-conquer approach to speed up the recovery process. However, the segmentation technique considered in the existing method operates with the assumption of a bi-modal distribution for the pixel values, but most images do not satisfy this condition. Therefore, in this work, an adaptive method was employed to perform more accurate segmentation, so that the real potential of the previous coefficient recovery methods can be unleashed. In addition, an improved rewritable adaptive data embedding method is also proposed that exploits the recoverability of coefficients. Discrete cosine transformation (DCT) patches and blocks for data hiding are judiciously selected based on the predetermined precision to control the embedding capacity and image distortion. Our results suggest that the adaptive coefficient recovery method is able to improve on the conventional method up to 27% in terms of CPU time, and it also achieved better image quality with most considered images. Furthermore, the proposed rewritable data embedding method is able to embed 20,146 bits into an image of dimensions 512×512.

Perceptual Image Representations for Support Vector Machine Image Coding

2011 ◽  
pp. 303-324
Author(s):  
Juan Gutiérrez ◽  
Gabriel Gómez-Perez ◽  
Jesús Malo ◽  
Gustavo Camps-Valls
Keyword(s):  
Support Vector Machine ◽  
Image Coding ◽  
General Procedure ◽  
Image Representation ◽  
Rate Distortion ◽  
Human Perception ◽  
Human Vision ◽  
Support Vector ◽  
Perception Model ◽  
Image Representations

Support vector machine (SVM) image coding relies on the ability of SVMs for function approximation. The size and the profile of the e-insensitivity zone of the support vector regression (SVR) at some specific image representation determines (a) the amount of selected support vectors (the compression ratio), and (b) the nature of the introduced error (the compression distortion). However, the selection of an appropriate image representation is a key issue for a meaningful design of the e-insensitivity profile. For example, in image coding applications, taking human perception into account is of paramount relevance to obtain a good rate-distortion performance. However, depending on the accuracy of the considered perception model, certain image representations are not suitable for SVR training. In this chapter, we analyze the general procedure to take human vision models into account in SVR-based image coding. Specifically, we derive the condition for image representation selection and the associated e-insensitivity profiles.

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