Image and video processing
BasicVideoTools
A Matlab Toolbox with convenient functions to handle video data. It includes routines to read VQEG and LIVE databases, generate synthetic sequences with controlled 2D and 3D speed, spatio-temporal Fourier transforms, perceptual sensors and filters (V1 and MT cells), and spatio-temporal CSFs.
References
- Importance of quantiser design compared to optimal multigrid motion estimation in video coding. Malo, J., Ferri, F.J., Gutierrez, J., and Epifanio, I. Electronics Letters, 36(9):807-809, 2000.
- Video quality measures based on the standard spatial observer. Watson, A.B., and Malo, J. ICIP, 2002.
SpatioSpectralTools
SpatioSpectralTools is a Matlab Toolbox for reflectance and illuminant estimation that uses spatial information to simplify the (otherwise ill-conditioned) inverse problem. The proposed analysis is useful to derive the spatio-spectral resolution required to solve a retrieval problem.
References
- The role of spatial information in disentangling the irradiance-reflectance-transmittance ambiguity. Jimenez, S., and Malo, J. IEEE Transactions on Geoscience and Remote Sensing, 52(8):4881-4894, 2014.
VideoCodingTools
VideoCodingTools is a Matlab Toolbox for motion estimation/compensation and video compression. Optical flow computation is done with perceptually meaningful hierarchical block matching, and residual quantization is done according to non-linear Human Visual System models.
References
- Importance of quantiser design compared to optimal multigrid motion estimation in video coding. Malo, J., Ferri, F.J., Gutierrez, J., and Epifanio, I. Electronics Letters, 36(9):807-809, 2000.
- Video quality measures based on the standard spatial observer. Watson, A.B., and Malo, J. ICIP, 2002.
VideoQualityTools
VideoQualityTools is a Matlab Toolbox for perceptual video quality assessment based on the Standard Spatial Observer model augmented with Divisive Normalization. It performed second-best in VQEG Phase-I using no ad-hoc hand-crafted features.
References
- Importance of quantiser design compared to optimal multigrid motion estimation in video coding. Malo, J., Ferri, F.J., Gutierrez, J., and Epifanio, I. Electronics Letters, 36(9):807-809, 2000.
- Video quality measures based on the standard spatial observer. Watson, A.B., and Malo, J. ICIP, 2002.
ViStaCoRe: Visual Statistics Coding and Restoration Toolbox
The ViStaCoRe Coding Package is a Matlab Toolbox for achromatic and color image compression that includes a set of transform coding algorithms based on (1) Human Vision Models of different accuracy, and (2) coefficient selection through Sparse Regression in local frequency domains (in particular SVR). The ViStaCoRe Restoration Package is a Matlab Toolbox for image restoration that includes (1) classical regularization techniques, (2) classical wavelet thresholding techniques, (3) regularization functionals based on non-linear Human Vision models, and (4) denoising techniques based on Kernel regression in wavelet domains.
References
- Image denoising with kernels based on natural image relations. Laparra, V., Gutiérrez, J., Camps-Valls, G., and Malo, J. Journal of Machine Learning Research, 11:873-903, 2010.
- On the suitable domain for SVM training in image coding. Camps-Valls, G., Gutiérrez, J., Gómez-Pérez, G., and Malo, J. Journal of Machine Learning Research, 9:49-66, 2008.
- Regularization operators for natural images based on nonlinear perception models. Gutiérrez, J., Ferri, F.J., and Malo, J. IEEE Transactions on Image Processing, 15(1):189-200, 2006.
- Nonlinear image representation for efficient perceptual coding. Malo, J., Epifanio, I., Navarro, R., and Simoncelli, E.P. IEEE Transactions on Image Processing, 15(1):68-80, 2006.
VistaQualityTools
VistaQualityTools is a Matlab Toolbox for full reference color (and also achromatic) image quality assessment based on divisive normalization Human Vision models in the DCT and the Wavelet domains.
References
- Divisive normalization image quality metric revisited. Laparra, V., Muñoz-Marí, J., and Malo, J. Journal of the Optical Society of America A: Optics and Image Science, and Vision, 27(4):852-864, 2010.