Local binary patterns based on directional wavelet transform for expression and pose-invariant face recognition

Anisotropic nonlocal means denoising

In this paper, we propose a new geometry representation method for multiview image sets. Our approach relies on graphs to describe the multiview geometry information in a compact and controllable way. The links of the graph connect pixels in different images and describe the proximity between pixels in 3D space. These connections are dependent on the geometry of the scene and provide the right amount of information that is necessary for coding and reconstructing multiple views. Our multiview image representation is very compact and adapts the transmitted geometry information as a function of the complexity of the prediction performed at the decoder side. To achieve this, our graph-based representation (GBR) carefully selects the amount of geometry information needed before coding. This is in contrast with depth coding, which directly compresses with losses the original geometry signal, thus making it difficult to quantify the impact of coding errors on geometry-based interpolation. We present the principles of this GBR and we build an efficient coding algorithm to represent it. We compare our GBR approach to classical depth compression methods and compare their respective view synthesis qualities as a function of the compactness of the geometry description. We show that GBR can achieve significant gains in geometry coding rate over depth-based schemes operating at similar quality. Experimental results demonstrate the potential of this new representation.

/pdf/graph-based-representation-for-multiview-image-geometry-3nnaza6328.pdf

Graph-Based Representation for Multiview Image Geometry

Image compression leads to minimize the storage-requirement of an image by reducing the size of the image. This paper presents a bit allocation strategy based on psychovisual threshold in image compression considering a similar idea of audio coding. In the audio coding, a dynamic bit allocation to each signal is related to the concept of variable block coding and bit allocation is performed on either a short block or long block of sample signals. Similarity, in our technique, more bits are assigned to a local block with visually-significant low frequency order, and fewer, with visually-insignificant high frequency order. This paper presents a bit allocation strategy based on psychovisual threshold in image compression. A psychovisual threshold is developed by minimizing the visual impact on the image quality degradation in image frequency coding. This paper investigates the error generated by the discrete cosine transform and sets the maximum acceptable error as a psychovisual threshold. The average reconstruction error per pixel on frequency order is utilized to prescribe a set of bit allocations which provide a significant improvement on the quality of image reconstruction at relatively low bit rates. The experimental results show that our dynamic bit-allocation technique in image compression manages to overcome artifact images in the image output. The proposed bit allocation strategy improves the quality of image reconstruction by about 20% compared to JPEG compression. This bit allocation strategy is designed to replace the traditional role of the quantization process in image compression.

Bit allocation strategy based on Psychovisual threshold in image compression

Local appearance-based face recognition using adaptive directional wavelet transform

Depth image-based rendering techniques for multiview applications have been recently introduced for efficient view generation at arbitrary camera positions. The rate control in an encoder has thus to consider both texture and depth data. However, due to different structures of depth and texture data and their different roles on the rendered views, the allocation of the available bit budget between them requires a careful analysis. Information loss due to tex- ture coding affects the value of pixels in synthesized views, while errors in depth information lead to a shift in objects or to unexpected patterns at their boundaries.In this paper, we address the problem of efficient bit allocation between texture and depth data of multiview sequences.We adopt a rate-distortion framework based on a simplified model of depth and texture images, which preserves the main features of depth and texture images. Unlike most recent solutions, our method avoids rendering at encoding time for distor- tion estimation so that the encoding complexity stays low.

/pdf/rate-distortion-analysis-of-multiview-coding-in-a-dibr-b0tdlr7m6d.pdf

Rate-distortion analysis of multiview coding in a DIBR framework

The inefficiency of separable wavelets in representing smooth edges has led to a great interest in the study of new 2-D transformations. The most popular criterion for analyzing these transformations is the approximation power. Transformations with near-optimal approximation power are useful in many applications such as denoising and enhancement. However, they are not necessarily good for compression. Therefore, most of the nearly optimal transformations such as curvelets and contourlets have not found any application in image compression yet. One of the most promising schemes for image compression is the elegant idea of directional wavelets (DIWs). While these algorithms outperform the state-of-the-art image coders in practice, our theoretical understanding of them is very limited. In this paper, we adopt the notion of rate-distortion and calculate the performance of the DIW on a class of edge-like images. Our theoretical analysis shows that if the edges are not “sharp,” the DIW will compress them more efficiently than the separable wavelets. It also demonstrates the inefficiency of the quadtree partitioning that is often used with the DIW. To solve this issue, we propose a new partitioning scheme called megaquad partitioning. Our simulation results on real-world images confirm the benefits of the proposed partitioning algorithm, promised by our theoretical analysis.

Rate-Distortion Analysis of Directional Wavelets

Depth image based rendering techniques for multiview applications have been recently introduced for efficient view generation at arbitrary camera positions. Encoding rate control has thus to consider both texture and depth data. Due to different structures of depth and texture images and their different roles on the rendered views, distributing the available bit budget between them however requires a careful analysis. Information loss due to texture coding affects the value of pixels in synthesized views while errors in depth information lead to shift in objects or unexpected patterns at their boundaries. In this paper, we address the problem of efficient bit allocation between textures and depth data of multiview video sequences. We adopt a rate-distortion framework based on a simplified model of depth and texture images. Our model preserves the main features of depth and texture images. Unlike most recent solutions, our method permits to avoid rendering at encoding time for distortion estimation so that the encoding complexity is not augmented. In addition to this, our model is independent of the underlying inpainting method that is used at decoder. Experiments confirm our theoretical results and the efficiency of our rate allocation strategy.

Rate-Distortion Analysis of Multiview Coding in a DIBR Framework

The inefficiency of separable wavelets in representing smooth edges has motivated the researchers to pursue new two dimensional transformations. One of the successful transformations in image compression is the directional wavelets. Although researchers have empirically shown that the directional wavelets outperform the separable wavelets in compression, there is no theoretical analysis to demonstrate this phenomena, specially when the directional wavelets are combined with partitioning algorithms such as quadtree. In this paper, we calculate the rate-distortion performance of the directional wavelets on a class of images. Our analysis shows that the quadtree partitioning deteriorates the performance. Therefore we propose another scheme, called megablocking. Our theoretical and simulation results confirm that megablocking outperforms the quadtree approach.

/pdf/rate-distortion-improvement-of-directional-wavelets-by-3zpb0qgkdl.pdf

Boshra Rajaei

Papers

Rate-distortion analysis of multiview coding in a DIBR framework

Rate-Distortion Analysis of Directional Wavelets

Rate-Distortion Analysis of Multiview Coding in a DIBR Framework

Rate-distortion improvement of directional wavelets by megablocking