This paper presents a new halftoning-based block truncation coding (HBTC) image reconstruction using sparse representation framework. The HBTC is a simple yet powerful image compression technique, which can effectively remove the typical blocking effect and false contour. Two types of HBTC methods are discussed in this paper, i.e., ordered dither block truncation coding (ODBTC) and error diffusion block truncation coding (EDBTC). The proposed sparsity-based method suppresses the impulsive noise

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... on ODBTC and EDBTC decoded image with a coupled dictionary containing the HBTC image component and the clean image component dictionaries. Herein, a sparse coefficient is estimated from the HBTC decoded image by means of the HBTC image dictionary. The reconstructed image is subsequently built and aligned from the clean, i.e. non-compressed image dictionary and predicted sparse coefficient. To further reduce the blocking effect, the image patch is firstly identified as "border" and "non-border" type before applying the sparse representation framework. Adding the Laplacian prior knowledge on HBTC decoded image, it yields better reconstructed image quality. The experimental results demonstrate the effectiveness of the proposed HBTC image reconstruction. The proposed method also outperforms the former schemes in terms of reconstructed image quality. INTRODUCTION Block truncation coding (BTC) and its variants have been playing an important role on image processing and computer vision applications, such as image/video compression [1-3], image watermarking [4, 5] , data hiding [3, 6], image retrieval and classification [7-10], image restoration, [11] [12] [13] etc. Many efforts have been focused on further improving the performance of BTC and its variants, including the computational complexity reduction, decoded image quality improvement, and its applications, as reported in [1, 2, 7-9, 12, 13]. The BTC-based image compression finds a new representation of an image to further reduce the storage requirement, and achieve a satisfactory coding gain. It is classified as a lossy image compression, in which a given image block is processed to yield a new representation consisting of two color quantizers and the corresponding bitmap image. The two color quantizers and bitmap image produced at the encoding stage are then transmitted to the decoder. The typical BTC techniques determine the two color quantizers, namely low and high means, by maintaining the intrinsic statistical properties of an image such as TELKOMNIKA Telecommun Comput El Control  Halftoning-based BTC image reconstruction using patch processing with border constraint (Heri Prasetyo) 395 first moment, second moment, etc. The corresponding bitmap image is simply obtained by applying the thresholding operation on each image block with the mean value of this processed block. This bitmap image consists of two binary values (0 and 1), in which the value 0 is replaced with the low mean value, whereas the value 1 is substituted with high mean value, in the decoding process. The BTC-based image compression can provide low computational complexity, however, it often suffers from the blocking effect and false contour issues [1, 2, 7, 12] . These problems make it less satisfactory for human perception. A new type of technique, namely halftoning-based block truncation coding (HBTC), has been proposed to overcome these problems. Figure 1 depicts the schematic diagram of HBTC technique. The HBTC substitutes the BTC bitmap image with the halftone image produced from specific image halftoning methods such as void-and-cluster halftoning [1], dithering approach [7-9], error diffusion technique [2, 10, 11] , dot diffused halftoning [12] , etc. This technique compensates the false contour and blocking effect problems by enjoying peculiar dither effects from the halftone image. In addition, the HBTC offers a lower computational complexity during the process of the two color quantizers determination. Herein, the color quantizers are simply replaced with the minimum and maximum pixel values found in an image block. Two popular HBTC methods, namely the ordered dithered block truncation coding (ODBTC) [1] and error diffusion block truncation coding (EDBTC) [2], have been developed and reported in the literature. The ODBTC and EDBTC change the BTC bitmap image with the halftone image produced from the ordered dithering and error diffused halftoning methods, respectively. Both of the ODBTC and EDBTC schemes yield better image quality compared to that of the classical BTC method as reported in [1, 2] . The two methods can be applied to other image processing and computer vision applications, including low computational image compression [1, 2, 11], content-based image retrieval [7-10], recognition of color building [14, 15] , blood image analysis [16] , object detection and tracking [17], etc.