Images of di®erent origin contain textures, and textural features in such regions are frequently employed in pattern recognition, image classi¯cation, information extraction, etc. Noise often present in analyzed images might prevent a proper solution of basic tasks in the aforementioned applications and is worth suppressing. This is not an easy task since even the most advanced denoising methods destroy texture in a more or less degree while removing noise. Thus, it is desirable to predict the¯ltering behavior before any denoising is applied. This paper studies the e±ciency of texture image denoising for di®erent noise intensities and several¯lter types under di®erent visual quality criteria (quality metrics). It is demonstrated that the most e±cient existing¯lters provide very similar results. From the obtained results, it is possible to generalize and employ the prediction strategy earlier proposed for denoising techniques based on the discrete cosine transform. Accuracy of such a prediction is studied and the ways to improve it