We propose an adaptive approach which performs blockiness reduction in both the DCT and spatial domains to reduce the block-to-block discontinuities. Blocking artifact detection and reduction is presented in this project. The algorithm first detects the regions of the image which present visible blocking artifacts. This detection is performed in the frequency domain and uses the estimated relative quantization error calculated when the discrete cosine transform (DCT) coefficients are modeled by a Laplacian probability function.Then, for each block affected by blocking artifacts, its dc and ac coefficients are recalculated for artifact reduction. To achieve this, a closed-form representation of the optimal correction of the DCT coefficients is produced by minimizing a novel enhanced form of the mean squared difference of slope for every frequency separately. This correction of each DCT coefficient depends on the eight neighboring coefficients in the subband-like representation of the DCT transform and is constrained by the quantization upper and lower bound. Experimental results illustrating the performance of the proposed method are presented and evaluate