Combined edge crispiness and statistical differencing for deblocking JPEG compressed images
In this work, a new approach is proposed that deals with blocking effects in JPEG compressed images. High-frequency details of the coded images are mainly contaminated by quantization noise. Preserving the image details and reducing the effect of quantization noise as much as possible can improve the ability of any enhancing method. To achieve this goal along with the removal of the blocking effect, the high-frequency components of the image are first extracted by high pass filtering. The result is then scaled by a factor that depends on the compression ratio and subtracted from the observed image. This result is used to design an adaptive filter that depends on the statistical behavior of the preprocessed image. The adaptive filter is applied to the resultant image. The result shows high SNR, significant improvement in the separation between blocking noise and image features, and effective reduction of image blurring. Other steps are required to preserve the global and local edges of the processed image, remove blocking noise, and ensure smoothness without blurring. These steps are dedicated to remove blocking artifacts and to enhance feature regularities. The evaluation of this approach in comparison with other techniques is carried out both subjectively and qualitatively.
deblocking adaptive filtering edge enhancement image restoration jpeg coding statistical analysis