A Novel Energy-Efficient Coding Based on Coordinated Group Signal Transformation for Image Compression in Energy-Starved Systems  
[PERSON]  
1 School of Photonics Engineering and Research Advances (SPERA), Ufa University of Science and Technology, 32 Z. Valiati Street, Ufa 450076, Russia; [EMAIL_ADDRESS] (E.G.) [PERSON]  
1 School of Photonics Engineering and Research Advances (SPERA), Ufa University of Science and Technology, 32 Z. Valiati Street, Ufa 450076, Russia; [EMAIL_ADDRESS] (E.G.) [PERSON]  
1 School of Photonics Engineering and Research Advances (SPERA), Ufa University of Science and Technology, 32 Z. Valiati Street, Ufa 450076, Russia; [EMAIL_ADDRESS] (E.G.) [PERSON]  
1 School of Photonics Engineering and Research Advances (SPERA), Ufa University of Science and Technology, 32 Z. Valiati Street, Ufa 450076, Russia; [EMAIL_ADDRESS] (E.G.) [PERSON]  
1 School of Photonics Engineering and Research Advances (SPERA), Ufa University of Science and Technology, 32 Z. Valiati Street, Ufa 450076, Russia; [EMAIL_ADDRESS] (E.G.) [PERSON]  
1 School of Photonics Engineering and Research Advances (SPERA), Ufa University of Science and Technology, 32 Z. Valiati Street, Ufa 450076, Russia; [EMAIL_ADDRESS] (E.G.) [PERSON]; [PERSON]  
1 School of Photonics Engineering and Research Advances (SPERA), Ufa University of Science and Technology, 32 Z. Valiati Street, Ufa 450076, Russia; [EMAIL_ADDRESS] (E.G.) [PERSON]  
1 School of Photonics Engineering and Research Advances (SPERA), Ufa University of Science and Technology, 32 Z. Valiati Street, Ufa 450076, Russia; [EMAIL_ADDRESS] (E.G.) [PERSON]  
1 School of Photonics Engineering and Research Advances (SPERA), Ufa University of Science and Technology, 32 Z. Valiati Street, Ufa 450076, Russia; [EMAIL_ADDRESS] (E.G.) [PERSON]  
1 School of Photonics Engineering and Research Advances (SPERA), Ufa University of Science and Technology, 32 Z. * System (or protocol) level;
* Radio interface (channel) level;
* Source encoding level [10; 11]. A network-level solution can be demonstrated by directing intra-sector traffic in mobile networks [12] or collaborating with neighboring wireless sensor network nodes to assist data transmission [13]. The protocol level includes downlink control [14], dynamic channel access [15], and uplink control [16] protocols. Optimizing MIMO technology, switching off subcarriers in orthogonal frequency division multiplexing [17; 18], and applying multidimensional constellation diagrams [19; 20] are examples of the channel-level methods. Finally, the source encoding level is related to various energy-efficient coding [21; 22; 23; 24], energy-efficient redundancy coding [25] techniques, and image compression (both lossy and lossless) [26; 27]. The rapid progress in multimedia and visual Internet of Things (IoT) technologies [28; 29], coupled with the growing reliance on microsatellites and unmanned aerial vehicles (UAVs) for remote sensing of the Earth [30; 31; 32], highlight the need for improved image compression technologies. Such advancements can significantly boost the energy efficiency of these systems. Autonomous energy-starved systems, such as satellites, UAVs, and IoT devices, can benefit from lossy image compression algorithms because they provide better compression, reducing power consumption. There are two types of lossy methods: transform-based and non-transform-based [27; 33]. However, it is noteworthy that these methods can be combined into one algorithm to improve image quality [34]. Transform-based methods convert images into another area, which allows for more efficient encoding and removal of imperceptible information. The most common transform-based methods use discrete cosine transform (DCT, JPEG compression method) and wavelet transform (JPEG2000 compression method) [35]. On the other hand, non-transform-based techniques are used to reduce the number of quantization levels [36; 37] and compress the dynamic range of image brightness values.