The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. There is always a need for generic architectures with better performance. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimension-wise selection of various height, width, and depth kernels. This single unit which uses the separable convolution concept provides multiple combinations using various dimension-based kernels. This single unit can be used for height-based, width-based, or depth-based dimensions; the same unit can even be used for height and width, width and depth, and depth and height dimensions. It can also be used for combinations involving all three dimensions of height, width, and depth. The main novelty of DBGC lies in the dimension selector block included in the proposed architecture. Proposed unoptimized kernel dimensions reduce FLOPs by around one third and also reduce the accuracy by around one half; semi-optimized kernel dimensions yield almost the same or higher accuracy with half the FLOPs of the original architecture, while optimized kernel dimensions provide 5 to 6% higher accuracy with around a 10 M reduction in FLOPs.

Digital visualization of human body in terms of medical images with high resolution and bit depth generates tremendous amount of data. In the field of medical diagnosis, lossless compression technique is preferred that facilitates efficient archiving and transmission of medical images avoiding false diagnosis. Among various approaches to lossless compression of medical images, predictive coding techniques have high coding efficiency and low complexity. Gradient Edge Detector (GED) used in predictive coding is based on threshold value for prediction and choice of threshold is very important for efficient prediction. However, no specific method is adopted in the literature for threshold value selection. This paper presents an efficient prediction solution targeted at lossless compression of 8 bits and higher bit depth volumetric medical images up to 16 bits. Novelty of the proposed technique is developing Resolution Independent Gradient Edge Predictor (RIGED) algorithm to support 8- and 16-bit depth medical images. Percentage improvement of the proposed model is 30.39% over state-of-the-art Median Edge Detector (MED) and 0.92% over Gradient Adaptive Predictor (GAP) in terms of entropy for medical image dataset of different modalities having different resolutions and bit depths.

The proposed block-based lossless coding technique presented in this paper targets at compression of volumetric medical images of 8-bit and 16-bit depth. The novelty of the proposed technique lies in its ability of threshold selection for prediction and optimal block size for encoding. A resolution independent gradient edge detector is used along with the block adaptive arithmetic encoding algorithm with extensive experimental tests to find a universal threshold value and optimal block size independent of image resolution and modality. Performance of the proposed technique is demonstrated and compared with benchmark lossless compression algorithms. BPP values obtained from the proposed algorithm show that it is capable of effective reduction of inter-pixel and coding redundancy. In terms of coding efficiency, the proposed technique for volumetric medical images outperforms CALIC and JPEG-LS by 0.70 % and 4.62 %, respectively.

The digitization of human body, especially for treatment of diseases can generate a large volume of data. This generated medical data has a large resolution and bit depth. In the field of medical diagnosis, lossless compression techniques are widely adopted for the efficient archiving and transmission of medical images. This article presents an efficient coding solution based on a predictive coding technique. The proposed technique consists of Resolution Independent Gradient Edge Predictor16 (RIGED16) and Block Based Arithmetic Encoding (BAAE). The objective of this technique is to find universal threshold values for prediction and provide an optimum block size for encoding. The validity of the proposed technique is tested on some real images as well as standard images. The simulation results of the proposed technique are compared with some well-known and existing compression techniques. It is revealed that proposed technique gives a higher coding efficiency rate compared to other techniques. Request access from your librarian to read this article's full text.