Images excessively contribute to communication in this era of multimedia. When a user transfers images over an unsecured communication network, then the absolute protection is a challenging issue to conserve the confidentiality of images. Encryption is a method of retaining the secrecy of images. This paper provides the succinct introduction to the cryptography, moreover, includes a concise description of various elemental securities' criteria of the image encryption algorithms. This work presents the survey of diverse image encryption techniques and comparison of discrete image encoding approaches, at last discloses a conclusion and suggests future works.

Recently, sentiment analysis (SA) has become more popular as it is crucial to moderate and examine the data from the internet. It contains several applications, such as social media monitoring, market research, and opinion mining. Aspect Based Sentiment Analysis (ABSA) is a domain of SA that manages sentiment at a better level. ABSA classifies sentiment in terms of all the aspects for obtaining superior insights as sentiment expressed. A major contribution has been developed in ABSA, and then this progress can be restricted only to some languages with suitable resources. One common method is to utilize machine learning (ML) approaches, namely Neural Networks (NN), Support Vector Machines (SVM), and Naive Bayes (NB), together with Asian and low language-specific resources. These resources offer data on the sentiment polarity (neutral, positive, or negative) of phrases and words that are generally utilized in low-resource languages. In this aspect, this study develops a new Pelican Optimization Algorithm with Deep Learning for ABSA (POADL-ABSA) on Asian and Low Resource Languages. The proposed POADL-ABSA technique focuses on the detection and classification of sentiments. To accomplish this, the POADL-ABSA technique encompasses various levels of operations such as pre-processed, feature vector conversion, and classification. In addition, the POADL-ABSA technique employs the BERT model for feature vector extraction. Besides, attention-based bi-directional long short-term memory (ABiLSTM) system was used for the recognition and classification of sentiments. Finally, the POA was utilized for optimum hyperparameter selection of the ABiLSTM model, and it helps in attaining enhanced sentiment classification results. To ensure the improvised performance of the IAOADL-ABSA technique, an extensive experimental outcome the IAOADL-ABSA technique surpassed other models with acc{u}_y , pre{c}_n , rec{a}_l , and {F}_{score} of 98.72%, 98.71%, 98.72%, and 98.71%, respectively. Therefore, the IAOADL-ABSA technique can be employed for accurate classification results.

Abstract-In computer vision saliency detection comprises wide range of methods to detect salient object present in the image. These methods focus how important object can be detect from the image. Results of these methods are fully dependent upon the type and quality of input image. In this paper saliency detection methods, Fixation prediction models, saliency map generated through various saliency detection algorithms and its evaluation measures are analyzed.Keyword -Saliency, Seam carving, Saliency Map, Fixation Prediction, Object Detection I. INTRODUCTION With the advancement of computer science and information technology information can be transfer with the use of images. Saliency region detection and object segmentation is also known as salient object detection. To preserve structure of important objects present in the image saliency detection algorithm are used. In image resizing accurate saliency map helpful to find out accurate salient object that must be preserve during image resizing in this way improved algorithm can be developed with accurate result where saliency is required to detect. Accurate saliency detection not only improves performance of the algorithm but also reduces computational time. Methods under saliency detection are classified in to two types of space domain based and frequency domain based [1]. Human visual system also has capability to provide information about salient object in the image. In computer vision saliency detection and salient object segmentation are the process through which most salient object can be detected and boundary of salient object can accurately defined [2]. Literature of saliency detection suggests two models: Top-down model and bottom up model.Bottom up approach pay more attention in color, intensities and edge orientation and top down approach pay more attention on high level features such as face, body and text and their structure. In order to produce better saliency map bottom up approach combines top down approach.Proposed models of saliency detection have been used to measure the exclusiveness of a location for a salient object which mostly attracts human attention [3]. Researchers are trying to improve the efficiency of salient map which can detect large salient region in image, well defined boundaries of object, uniformly highlight full salient object, disregard noise and other blocking artifacts in the image and also should be capable to output full resolution saliency map. Most of the methods in saliency detection are useful for detection of region of interest (ROI). ROI of an image contains pixels which are similar to each other and are capable to fascinate attention of human eyes.In [4], Liu et al. proposed region based pixel-wise saliency detection algorithm which can detect prominent object or region from the image and generate full resolution energy map. Saliency detection models and algorithm suggested by the researchers are widely used in various multimedia disciplines like image compression [5], content aware image resizing [2], image adapt...

With the advancement in the field of image processing, images are being processed using various image processing algorithms. Nowadays, many efficient content-aware image resizing techniques are being used to safeguard the prominent regions and to generate better results that are visually appealing and pleasing while resizing. Advancements in the new display device with varying screen size demands the development of efficient image resizing algorithm. This paper presents a survey on various image retargeting methods, comparison of image retargeting results based on performance, and also exposes the main challenges in image retargeting such as content preservation of important regions, distortion minimization, and improving the efficiency of image retargeting methods. After reviewing literature from researchers it is suggested that the use of the single operator in image retargeting such as scaling, cropping, seam carving, and warping is not sufficient for obtaining satisfactory results, hence it is essential to combine multiple image retargeting operators. This survey is useful for the researchers interested in content-aware image retargeting.

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