Image compression using JPEG with reduced blocking effects via adaptive down-sampling and self-learning image sparse representation

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Blocking is an annoying effect in image compression using JPEG especially at low bit-rates. In this paper, a two-phase method is proposed for reducing JPEG blocking effects. In the first phase, the image is adaptively down-sampled via assessing the DCT coefficients of the image blocks. Since the blocks have a fixed size, independent to the size of the image, down-sampling can reduce the number of blocks, and hence reduce the blocking effects. Appropriate down-sampling before compression can improve coding performance, especially at low bit-rate. The decoder, after decompression, up-samples the image to its original resolution. Although, down-sampling alleviates the blocking effects, yet some blocking effects are remained in the result and the image is damaged due to resizing. Hence, self-learning sparse representation is applied in the second phase for a better deblocking and also for alleviating the degradation due to resizing. Experimental results demonstrate that the proposed method can efficiently improve the subjective and objective quality of JPEG compressed images at low bit-rates and outperforms the existing methods.