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When we talk about steganographic algorithms, it is imperative to study the quality of the image hosting and image retrieval, and is also necessary to consider the robustness of the algorithm. This paper presents the experimental results obtained by applying a steganographic algorithm to RGB images. The measures used are qualitative and quantitative related to the multichannel of Human Vision System. When this algorithm is employed we see that the numerical calculations performed by the computer cause errors and alterations in the images chosen, so we applied a scaling factor depending on the number of bits of the image to adjust these errors.

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We present an automatic program of blue whale photo-identification for mobile devices. The proposed technique works in the wavelet domain to reduce the image size and the processing time of the proposed algorithm, and with an edge enhancement filter, the characteristics of the blue whale are preserved. Additionally, an image palette reduction algorithm based on local image complexity estimation is introduced to eliminate redundant colors, thus decreasing the number of pixels that are bad classified in the segmentation process and minimizing the resource consumption of the mobile device. The segmented image is obtained with the FCM (fuzzy C-means) or K-means algorithms incorporating a dynamic filtering which is proposed in this paper to improve the brightness and contrast of the acquired image increasing the performance of the image segmentation. Experimental results show that the proposed approach potentially could provide a real-time solution to photo-id of blue whale images and it can be transportable and portable power for mobile devices. Finally, the proposed methodology is simple, efficient, and feasible for photo-id applications in mobile devices.