Bicubic interpolation

About: Bicubic interpolation is a research topic. Over the lifetime, 3348 publications have been published within this topic receiving 73126 citations.

Image interpolation by rational ball cubic B-spline representation and genetic algorithm

Abstract: In this article a rational ball cubic B-spline representation based image interpolation scheme is proposed with tension parameter to interpolate two dimensional natural images. Genetic algorithm is used to detect the optimal value of tension parameter, so that sum square error is minimum. Comparison among the proposed interpolation scheme and the existing image interpolation techniques in terms of some state-of-the-art image quality metrics with classical Peak Signal-to-Noise Ratio (PSNR), depicts that suggested ball cubic B-spline form produces better results and is more suitable for problems related to image interpolation.

Robust Color Image Hashing Using Quaternion Polar Complex Exponential Transform for Image Authentication

Abstract: Image hashing is one of the multimedia protection techniques. In this paper, a new method for robust image hashing based on quaternion polar complex exponential transform (QPCET) is proposed. The proposed method targets two goals. The first goal is the robustness against geometric and common signal processing attacks. The second one is authenticating color images without conversion which keeps their color information. In the proposed method, the input color image is normalized by the bicubic interpolation and then the interpolated image passes to Gaussian low-pass filter. QPCET moments are used to extract features. Finally, the hash value is calculated using the extracted features. On the sender side, a secret key is utilized to increase the protection of the hash value before transmitting it. The hash value is attached with the transmitted color image. On the receiver side, the authenticity of the received image is checked by decrypting the hash value. Euclidean distance is used to check the similarity between different hashes. Results of the conducted experiments prove the robustness of proposed hash against different geometric and signal processing attacks. Also, it preserves the content of the transmitted color image. Hashing different images has a very low collision probability which ensure the suitability of the proposed method for image authentication. Comparison with the existing methods ensures the superiority of the proposed method.

Intermediate image interpolation using polyphase weighted median filters

Abstract: A new algorithm for the interpolation of temporal intermediate images using polyphase weighted median filters is proposed in this paper. To achieve a good interpolation quality not only in still but also in moving areas of the image, vector based interpolation techniques have to be used. However, motion estimation on natural image scenes always suffers from errors in the estimated motion vector field. Therefore it is of great importance that the interpolation algorithm possesses a sufficient robustness against vector errors. Depending on the input and output frame repetition rate, different cyclically repeated interpolation phases can be distinguished. The new interpolation algorithm uses dedicated weighted median filters for each interpolation phase (polyphase weighted median filters) which are (due to their shift property) able to achieve a correct positioning of moving edges in the interpolated image, even if the estimated vector differs from the true motion vector up to a certain degree. A new design method for these dedicated error tolerant weighted median filters is presented in the paper. Other aspects like e.g. the preservation of fine image details can also be regarded in the design process. The results of the new algorithm are compared to other existing interpolation algorithms.

Comments on "Winscale: an image-scaling algorithm using an area pixel Model"

Abstract: In the paper by Kim et al. (2003), the authors propose a new image scaling method called winscale. The presented method can be used for scaling up and down. However, scaling down utilizing the winscale concept gives exactly the same results as the well-known bilinear interpolation. Furthermore, compared to bilinear, scaling up with the proposed winscale "overlap stamping" method has very similar calculations. The basic winscale upscaling differs from the bilinear method.