State-of-the-Art and Trends in Scalable Video Compression With Wavelet-Based Approaches

Adami, Nicola; Signoroni, Alberto; Leonardi, Riccardo

doi:10.1109/tcsvt.2007.906828

Cited by 82 publications

(46 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conversely, the top-hat transformed image is the residual of the opened image to the original image. In other words, top-hat transformed image is comparable to noise at small scale, just as demonstrated in (18). Such an oscillation is caused by the existence of noise, but the trend of the mutual information of (21) is increasing.…”

Section: Analysis Of the I-de Algorithmmentioning

confidence: 77%

“…The first one is the wavelet transform [15], which is widespread used in the field of image processing, such as image denosing [16], image and video compression [17,18], and multi-modality registration [19]. By employing the windowing technique with variable-size region, wavelet transform is capable of decomposing a signal into multi-scale representation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mutual Information Criterion-Based Optimal Scale Selection for Image Denoising and Segmentation

Wen¹,

Zhu²,

Gu³

2013

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

Scale-spaces play an important role in many computer vision tasks. Automatic scale selection is at the foundation of multiscale image analysis, but its performance is still very subjective and empirical. To automatically select the appropriate scale for a particular issue, a scale selection model based on information theory is proposed in this paper. The proposed model utilizes mutual information as a measuring criterion of similarity for the optimal scale selection in multi-scale analysis, with applications to image denoising and segmentation. Firstly, we focus on the morphological operator based scale selection to image denoising. This technique does not require the prior knowledge of the noise variance and can effectively eliminate the variation of illumination. Secondly, we develop a clustering based unsupervised image segmentation algorithm by recursively pruning the Huffman coding tree. The proposed clustering algorithm can preserve the maximum amount of information at a specific clustering number from the information-theoretical point of view. Finally, for the feasibility of the proposed algorithms, its theoretical properties are analyzed mathematically and its performance is tested by a series of experiments, which demonstrate that it yields the optimal scale for our developed image denoising and segmentation algorithms.

show abstract

Section: Analysis Of the I-de Algorithmmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Mutual Information Criterion-Based Optimal Scale Selection for Image Denoising and Segmentation

Wen¹,

Zhu²,

Gu³

2013

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Applying this process iteratively on the smooth coefficient leads to a multiresolution analysis (MRA) [20] of the original signal. Due to its simplicity, some directional transforms based on lifting have been proposed in the literature for image [21]- [23] and video [24]- [29] representation.…”

Section: B Related Workmentioning

confidence: 99%

“…Then, a 2-dimensional wavelet transform is carried out in the spatial domain [24]. In the latter, each frame is firs wavelet transformed in the spatial domain, followed by MCTF.…”

Section: B Related Workmentioning

confidence: 99%

Directional Transforms for Video Coding Based on Lifting on Graphs

Martinez-Enriquez

Cid-Sueiro

Díaz-de-María

et al. 2018

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

Abstract-In this work we describe and optimize a general scheme based on lifting transforms on graphs for video coding. A graph is constructed to represent the video signal. Each pixel becomes a node in the graph and links between nodes represent similarity between them. Therefore, spatial neighbors and temporal motion-related pixels can be linked, while nonsimilar pixels (e.g., pixels across an edge) may not be. Then, a lifting-based transform, in which filterin operations are performed using linked nodes, is applied to this graph, leading to a 3-dimensional (spatio-temporal) directional transform which can be viewed as an extension of wavelet transforms for video. The design of the proposed scheme requires four main steps: (i) graph construction, (ii) graph splitting, (iii) filte design, and (iv) extension of the transform to different levels of decomposition. We focus on the optimization of these steps in order to obtain an effective transform for video coding. Furthermore, based on this scheme, we propose a coefficien reordering method and an entropy coder leading to a complete video encoder that achieves better coding performance than a motion compensated temporal filterin wavelet-based encoder and a simple encoder derived from H.264/AVC that makes use of similar tools as our proposed encoder (reference software JM15.1 configu ed to use 1 reference frame, no subpixel motion estimation, 16 × 16 inter and 4 × 4 intra modes).

show abstract

“…Although in the past both approaches have been used but lately there is more emphasis on wavelet video coding using 3-D wavelet transform. However, recent investigations have shown that open-loop architecture of 3-D wavelet transform has its own drawback which can be overcome by using closed-loop architecture only [8]. Further, storage requirement and the large delay involved limits its use for low memory, delay sensitive video services.…”

Section: Introductionmentioning

confidence: 99%