EVALUATION OF DLX MICROPROCESSOR INSTRUCTIONS EFFICIENCY FOR IMAGE COMPRESSION

Internet of things (IoT) nowadays uses a generic microprocessor, which is applicable for general purpose and uses many machine instructions, thus it is causing a high power. Likewise, IoT can also be integrated on ASIC (Application-specific integrated circuit) which is customized for partial use. ASIC is hardcoded, meaning that the program cannot be modified, therefore it tends to consume less power compared to generic microprocessor. This thesis considers a compression for an image of CCTV, which is using a microprocessor that is designed for application specific as the compression. Compressing image is required to reduce the size of the original image. This thesis uses the Deluxe (DLX) microprocessor with a high performance to design an image compressor, and the machine instructions were determined with a specific algorithm. The compression uses Joint Photographic Experts Group (JPEG) format lossy compression, which is the most commonly used to compress multimedia data. The proposed compression method is Huffman Coding, coded in the assembly DLX programming language. DCT and Quantization are needed to be simulated in Matlab to do the Huffman coding process. Then, the result data can be processed into Huffman. The result of this stage is by using Huffman Coding in the DLX microprocessor, it requires total of 11657 cycles executed by 8622 instructions. Thus, with such specific machine instructions, the performance of DLX microprocessor to execute Huffman Coding can be efficient. Keywords: IoT, DLX microprocessor, Huffman Coding, image compression, JPEG

High capacity steganographic method based upon JPEG

The two most important aspects of any image-based steganographic system are the quality of the stegoimage and the capacity of the cover image. This paper proposes a novel and high capacity steganographic approach based on Discrete Cosine Transformation (DCT) and JPEG compression. JPEG technique divides the input image into non-overlapping blocks of 8x8 pixels and uses the DCT transformation. However, our proposed method divides the cover image into nonoverlapping blocks of 16x16 pixels. For each quantized DCT block, the least two-significant bits (2-LSBs) of each middle frequency coefficient are modified to embed two secret bits. Our aim is to investigate the data hiding efficiency using larger blocks for JPEG compression. Our experiment result shows that the proposed approach can provide a higher information hiding capacity than Jpeg-Jsteg and Chang et al. methods based on the conventional blocks of 8x8 pixels. Furthermore, the produced stego-images are almost identical to the original cover images

JPEG steganography: A performance evaluation of quantization tables

The two most important aspects of any image based steganographic system are the imperceptibility and the capacity of the stego image. This paper evaluates the performance and efficiency of using optimized quantization tables instead of default JPEG tables within JPEG steganography. We found that using optimized tables significantly improves the quality of stego-images. Moreover, we used this optimization strategy to generate a 16x16 quantization table to be used instead of that suggested. The quality of stego-images was greatly improved when these optimized tables were used. This led us to suggest a new hybrid steganographic method in order to increase the embedding capacity. This new method is based on both and Jpeg-Jsteg methods. In this method, for each 16x16 quantized DCT block, the least two significant bits (2-LSBs) of each middle frequency coefficient are modified to embed two secret bits. Additionally, the Jpeg-Jsteg embedding technique is used for the low frequency DCT coefficients without modifying the DC coefficient. Our experimental results show that the proposed approach can provide a higher information-hiding capacity than the other methods tested. Furthermore, the quality of the produced stego-images is better than that of other methods which use the default tables

Fast object detection in compressed JPEG Images

Object detection in still images has drawn a lot of attention over past few years, and with the advent of Deep Learning impressive performances have been achieved with numerous industrial applications. Most of these deep learning models rely on RGB images to localize and identify objects in the image. However in some application scenarii, images are compressed either for storage savings or fast transmission. Therefore a time consuming image decompression step is compulsory in order to apply the aforementioned deep models. To alleviate this drawback, we propose a fast deep architecture for object detection in JPEG images, one of the most widespread compression format. We train a neural network to detect objects based on the blockwise DCT (discrete cosine transform) coefficients {issued from} the JPEG compression algorithm. We modify the well-known Single Shot multibox Detector (SSD) by replacing its first layers with one convolutional layer dedicated to process the DCT inputs. Experimental evaluations on PASCAL VOC and industrial dataset comprising images of road traffic surveillance show that the model is about $2\times$ faster than regular SSD with promising detection performances. To the best of our knowledge, this paper is the first to address detection in compressed JPEG images