Software applications designed for mobile devices utilizing the Android operating system that enable the manipulation of unprocessed image data are the subject of this discussion. These applications allow users to access and modify image files containing the complete, minimally processed data captured directly from a device’s camera sensor. For instance, a photographer might use such an application to adjust exposure, white balance, and other parameters before converting the image to a more common format like JPEG.
The ability to work with unprocessed image data on mobile devices offers significant advantages. It provides greater control over the final image’s appearance, facilitating correction of errors or application of specific artistic styles. Furthermore, this functionality can be crucial in preserving image detail and dynamic range, characteristics that are often compromised during standard image processing. The emergence of these applications reflects the increasing computational power of mobile devices and the growing demand for professional-level image editing capabilities on the go.
The subsequent sections will delve into specific features offered by such applications, common use cases, the technical considerations involved in processing this data, and a comparison of popular choices available for the Android platform.
1. Image Data Access
Image data access forms the bedrock upon which any raw file editing application for Android functions. The ability to directly read and interpret unprocessed image sensor data is the fundamental requirement. Without this capability, the core functionality of manipulating parameters like exposure, white balance, and color would be unattainable.
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Direct Sensor Data Reading
Applications must bypass Android’s standard image processing pipeline to retrieve data directly from the camera sensor. This involves accessing the raw Bayer pattern data, which represents the light intensity values captured by individual sensor pixels. Failure to properly interpret this pattern will result in corrupted or unusable images.
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Format Support
Diverse camera manufacturers employ various raw file formats (e.g., DNG, CR2, NEF). An application’s utility is directly proportional to the breadth of formats it can interpret. Limited format support restricts users to specific camera models or requires external format conversion steps, hindering workflow efficiency.
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Memory Management
Unprocessed image files can be significantly larger than their JPEG counterparts. Efficient memory management is crucial for smooth operation, especially on resource-constrained mobile devices. Poorly optimized applications may suffer from slowdowns, crashes, or the inability to handle high-resolution images.
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Hardware Acceleration
Decoding and processing large raw image datasets can be computationally intensive. Leveraging hardware acceleration, such as GPU processing, can significantly improve performance. Applications that fail to utilize available hardware resources may offer a suboptimal user experience.
In summary, effective image data access, encompassing direct sensor data reading, broad format support, efficient memory handling, and hardware acceleration, are critical determinants of a raw file editing application’s overall functionality and usability on the Android platform. These capabilities directly influence the user’s ability to manipulate and optimize image data, making them indispensable components of a comprehensive raw editing workflow.
2. White Balance Control
White balance control is a crucial feature within any Android application designed for raw file editing. It enables the correction of color casts arising from varying lighting conditions present during image capture, allowing for a more accurate and aesthetically pleasing representation of the scene.
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Color Temperature Adjustment
This facet allows for the manipulation of the image’s overall warmth or coolness by adjusting the color temperature, typically measured in Kelvin. A low color temperature adds a warm, yellowish tone, suitable for mimicking sunset lighting, while a high color temperature introduces a cool, bluish tone, replicating daylight or shade. Within a raw file editor, adjusting this parameter accurately represents the intended color balance, especially in situations where the camera’s automatic white balance failed to correctly interpret the scene. For example, correcting the excessive warmth in an indoor shot taken under incandescent lighting brings the image closer to natural colors.
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Tint Correction
Tint adjustment corrects for deviations along the green-magenta axis, addressing color casts that may not be resolved by color temperature adjustments alone. Fluorescent lighting, for instance, often introduces a green cast, which can be counteracted by adding magenta. Precisely controlling tint within a raw editor is essential for achieving accurate color reproduction, particularly when dealing with complex or mixed lighting scenarios.
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Presets and Custom Profiles
Many raw file editors offer a selection of white balance presets tailored to common lighting conditions such as daylight, cloudy, tungsten, and fluorescent. Additionally, the ability to create and save custom white balance profiles allows users to consistently apply specific corrections to images captured under similar conditions. This is valuable for photographers who regularly shoot in controlled environments or prefer a particular aesthetic.
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Color Channel Mixing
Advanced raw file editors may provide granular control over individual color channels (red, green, blue) within the white balance adjustment. This allows for highly targeted color correction, enabling the subtle removal of specific color casts or the creation of unique color effects. While requiring a deeper understanding of color theory, channel mixing offers unparalleled precision in white balance manipulation.
The precise and flexible white balance control afforded by raw file editing applications for Android is instrumental in achieving accurate color representation and realizing creative visions. These functionalities empower users to overcome the limitations of automatic white balance systems and tailor the image’s color to their specific needs and preferences.
3. Exposure Adjustment
Exposure adjustment constitutes a critical element within any Android application designed for the manipulation of unprocessed image data. It addresses the overall brightness of an image by modifying the amount of light recorded, effectively simulating adjustments to aperture, shutter speed, and ISO sensitivity parameters traditionally controlled within a camera. Within a raw file editor, the ability to adjust exposure after capture allows for the recovery of detail in both overexposed and underexposed areas of an image. For example, a photograph taken in bright sunlight may exhibit blown-out highlights; through exposure adjustment, the user can reduce the overall brightness, bringing back detail in the sky or other bright areas. Conversely, a photo taken in low light, resulting in underexposure, can be brightened to reveal previously obscured details in the shadows.
The significance of exposure adjustment extends beyond simple brightness correction. It impacts the dynamic range of the image, affecting the perceived contrast and the level of detail visible in both the brightest and darkest regions. Precise control over exposure allows for the optimization of tonality, ensuring a balanced representation of the scene. Further, when coupled with other raw processing tools, such as highlight and shadow recovery, exposure adjustment becomes a potent tool for achieving specific aesthetic effects. For instance, a user might deliberately underexpose an image to create a moodier, more dramatic atmosphere, subsequently recovering shadow detail to maintain visual interest. This level of control is unattainable with standard JPEG editing due to the limited dynamic range inherent in that format.
In summary, exposure adjustment within raw file editing applications for Android is essential for correcting exposure errors and creatively shaping the appearance of an image. It provides a far greater degree of control than traditional image editing, allowing users to maximize the potential of their camera’s raw data and achieve professional-quality results on mobile devices. The effectiveness of this adjustment, however, is directly tied to the quality of the application’s processing algorithms and the dynamic range captured by the device’s camera sensor.
4. Noise Reduction
Noise reduction is an indispensable function within applications designed for editing unprocessed image data on Android devices. The presence of noise, characterized by unwanted graininess or artifacts, is particularly pronounced in raw files due to the absence of in-camera noise reduction processing that is typically applied to JPEG images. Therefore, robust noise reduction capabilities are essential for realizing the full potential of raw image data on mobile platforms.
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Luminance Noise Reduction
Luminance noise manifests as random variations in brightness across the image, particularly noticeable in shadow areas. Applications for editing unprocessed data on Android must employ algorithms capable of effectively smoothing these variations without sacrificing image detail. Excessive luminance noise reduction can lead to a loss of sharpness and an unnatural “plastic” appearance. Therefore, algorithms must strike a balance between noise suppression and detail preservation. For instance, images captured in low-light conditions at high ISO settings will benefit significantly from precise luminance noise reduction.
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Color Noise Reduction
Color noise appears as random color speckles throughout the image. This is often more distracting than luminance noise and requires specialized algorithms for effective mitigation. Color noise reduction functions by identifying and suppressing aberrant color values, resulting in a cleaner and more visually appealing image. Overly aggressive color noise reduction, however, can desaturate the image and lead to a loss of color vibrancy. Raw file editors for Android must offer precise control over color noise reduction parameters to avoid these artifacts. An example would be addressing the blotchy color noise visible in long-exposure night shots.
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Detail Preservation
A critical challenge in noise reduction is preserving fine details within the image. Algorithms must differentiate between genuine image information and noise, avoiding the blurring or removal of important textural elements. Sophisticated raw file editors on Android incorporate techniques such as edge-aware smoothing and frequency-based noise separation to minimize detail loss. This is particularly important for images containing intricate patterns or fine textures, such as architectural details or foliage.
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Algorithm Efficiency
Noise reduction is computationally intensive, especially when processing large raw files. Android applications must employ optimized algorithms that can deliver effective noise reduction without causing excessive processing delays or battery drain. The use of hardware acceleration, such as GPU processing, can significantly improve performance. Efficient noise reduction algorithms are crucial for maintaining a fluid and responsive editing experience on mobile devices.
The interplay between these facets of noise reduction defines the usability and effectiveness of applications designed for manipulating raw data on the Android platform. Applications offering precise control over luminance and color noise reduction, coupled with robust detail preservation techniques and efficient algorithm implementation, empower users to extract maximum image quality from their mobile devices. The absence of effective noise reduction limits the utility of raw file editing, particularly when dealing with images captured in challenging lighting conditions or using high ISO settings.
5. Sharpening Tools
Sharpening tools are a critical component of any raw file editor for Android, functioning to enhance the perceived clarity and detail within an image. Raw files, being unprocessed data directly from the camera sensor, often appear softer than their JPEG counterparts due to the absence of in-camera sharpening. Therefore, the ability to apply controlled sharpening is essential for achieving visually appealing and technically sound results. The effect of inadequate or absent sharpening tools is immediately noticeable: images lack definition, fine details become indistinct, and overall visual impact is diminished. Conversely, over-sharpening leads to the introduction of unwanted artifacts, such as halos around edges and the amplification of noise.
Sharpening within a raw file editor operates by increasing the contrast along edges within the image. Various algorithms, such as unsharp masking and deconvolution sharpening, are employed to achieve this effect. Unsharp masking, a common technique, works by creating a blurred negative of the image, then subtracting this blurred version from the original, thus accentuating edges. Deconvolution methods, on the other hand, attempt to reverse the blurring introduced by the camera’s lens and sensor. The choice of sharpening algorithm and the degree to which it is applied significantly impacts the final image. For example, sharpening a landscape photograph might involve enhancing the texture of rocks and foliage, while sharpening a portrait requires careful consideration to avoid exaggerating skin imperfections. Mobile raw editors must offer fine-grained control over sharpening parameters, allowing users to tailor the effect to the specific characteristics of each image.
In conclusion, sharpening tools represent a vital stage in the raw image processing workflow on Android devices. Their correct application rectifies the inherent softness of unprocessed data, bringing out the intended level of detail. The challenge lies in achieving a balance between sharpness and artifact introduction, necessitating precise control over sharpening parameters and a discerning eye. The absence of effective sharpening features significantly limits the potential of a raw file editor, rendering it incapable of producing professional-quality results.
6. Color Grading
Color grading constitutes a pivotal stage in the post-processing workflow facilitated by raw file editors on the Android platform. It extends beyond simple color correction, encompassing the manipulation of an image’s aesthetic color palette to evoke specific moods, establish visual styles, and enhance storytelling. In the context of mobile raw image processing, color grading empowers users to transcend the limitations of the in-camera image and imbue it with a distinct artistic signature.
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Hue, Saturation, and Luminance (HSL) Adjustments
HSL adjustments provide granular control over individual color ranges within an image. Hue determines the base color (e.g., shifting reds towards oranges), saturation controls the color’s intensity, and luminance dictates its brightness. Within a raw file editor, HSL adjustments allow for targeted color manipulation without affecting other color ranges. For instance, a user might selectively increase the saturation of a blue sky or darken the luminance of green foliage to create a more visually striking landscape. The ability to isolate and manipulate specific colors is crucial for achieving nuanced color grading effects.
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Color Balance and Split Toning
Color balance controls the overall color cast of an image, allowing for adjustments along the red-cyan, green-magenta, and blue-yellow axes. Split toning allows users to apply different color tints to the highlights and shadows of an image. This technique is frequently used to create vintage or cross-processed effects. Within a raw file editor, color balance corrects for color imbalances caused by lighting conditions, while split toning adds stylistic color accents. An example might involve adding a warm tint to the highlights and a cool tint to the shadows to create a sense of depth and contrast.
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Color Look-Up Tables (LUTs)
LUTs are pre-defined color transformations that can be applied to an image to instantly alter its color palette. These tables map input color values to output color values, creating a specific color grading style. Raw file editors on Android often support the import and application of custom LUTs, allowing users to quickly achieve complex color grading effects. LUTs can emulate the look of film stocks, replicate the color palettes of popular movies, or create entirely unique visual styles. They provide a convenient way to apply consistent color grading across multiple images or projects.
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Curves Adjustments
Curves adjustments offer precise control over the tonal range and color balance of an image. A curves tool allows users to manipulate the relationship between input and output color values, creating subtle or dramatic changes in contrast, brightness, and color. Within a raw file editor, curves adjustments are used to fine-tune the overall tone and color of an image, correct exposure problems, and create stylized color effects. For example, an S-shaped curve increases contrast, while a reversed S-shaped curve reduces contrast. Curves adjustments require a deeper understanding of image tonality but offer unparalleled precision in color and tonal manipulation.
These color-grading tools are essential to Raw file editors on the android platform. These are also used for the creation of unique color palettes and precise control over images.
7. Format Compatibility
The functionality of a raw file editor for Android is fundamentally contingent upon its format compatibility. The raw file format, varying across camera manufacturers (e.g., Canon’s CR2, Nikon’s NEF, Sony’s ARW, Adobe’s DNG), dictates how image data is stored and interpreted. A raw file editor’s primary purpose is to decode this data and provide tools for its manipulation. If the application lacks compatibility with a particular raw format, it is incapable of processing images from cameras that utilize that format. This lack of compatibility directly hinders the user’s ability to edit images taken with those specific devices, rendering the application effectively useless for that particular workflow. For example, an editor lacking support for Sony’s ARW format cannot be used by a photographer who exclusively uses Sony cameras that record in that format. Consequently, format compatibility is not merely a desirable feature but an indispensable requirement for a functional raw file editor.
The implication of limited format compatibility extends beyond simple usability. It directly impacts workflow efficiency and archival capabilities. A photographer relying on a raw file editor with narrow format support may be forced to transcode raw files into a more universally compatible format, such as DNG. This transcoding process introduces an additional step into the workflow, increasing processing time and potentially degrading image quality. Furthermore, reliance on proprietary formats carries inherent risks related to long-term archival. If a camera manufacturer ceases support for a particular raw format, accessing and editing those files in the future becomes significantly more challenging. DNG, being an open standard, mitigates this risk, but its adoption is not universal. Real-world scenarios where photographers transition between camera brands or collaborate with individuals using different equipment highlight the need for robust and versatile format support.
In summation, format compatibility is not merely a technical detail but a foundational element determining the utility and longevity of a raw file editor for Android. The ability to process a diverse range of raw formats ensures broader usability, streamlines workflows, and safeguards long-term image accessibility. While challenges exist in keeping pace with the ever-evolving landscape of camera technology and file formats, comprehensive format support remains a key differentiator and a critical factor in evaluating the suitability of any raw file editing application.
8. Non-destructive Editing
Non-destructive editing represents a core principle in raw file editors for Android, wherein alterations made to an image do not directly modify the original file. This approach preserves the integrity of the initial raw data, enabling iterative adjustments and experimentation without permanent alterations. The direct cause of implementing non-destructive editing stems from the inherent nature of raw files, which contain the complete, minimally processed data captured by the camera sensor. Permanently altering this data would limit future editing possibilities and potentially degrade image quality, particularly when attempting to revert to previous states. The importance of non-destructive editing lies in its ability to provide a safety net, allowing users to explore various editing options without fear of irreversibly damaging their original image. A practical example involves adjusting exposure; should the initial adjustment prove excessive, the user can simply revert to the original exposure value without compromising the underlying raw data. This capability is particularly crucial when using mobile devices, where processing power and screen size may limit precise adjustments on the first attempt.
The practical application of non-destructive editing relies on storing editing instructions as metadata separate from the image file itself. This metadata, typically saved in XMP sidecar files or within the application’s internal database, contains a record of all adjustments made, such as exposure, white balance, and sharpening. When the image is opened within the editor, these instructions are applied to the raw data in real-time, creating a preview of the edited image. The original raw file remains untouched. This methodology allows users to export multiple versions of the same image with different edits, all while maintaining the original data’s integrity. A user might, for example, create one version optimized for social media and another intended for print, each derived from the same untouched raw file. The absence of non-destructive editing would necessitate creating duplicates of the raw file for each editing iteration, consuming significant storage space and complicating file management.
In conclusion, non-destructive editing is an indispensable component of raw file editors for Android, ensuring flexibility and preserving the integrity of original image data. While the implementation of this feature introduces technical challenges related to metadata management and processing overhead, the benefits in terms of editing freedom and long-term archival far outweigh the drawbacks. Understanding the principles of non-destructive editing is crucial for leveraging the full potential of raw file editing on mobile devices and achieving professional-quality results. Its absence would severely limit the versatility and practical value of these applications.
9. Metadata Handling
Metadata handling is an intrinsic component of raw file editors for Android, functioning as the mechanism by which image-related information is preserved and managed throughout the editing process. Raw files contain significantly more than just pixel data; they also encapsulate a wealth of metadata, including camera settings (aperture, shutter speed, ISO), lens information, date and time of capture, GPS coordinates, and copyright details. Proper metadata handling ensures that this information is not lost or corrupted during editing, thereby maintaining the integrity and contextual relevance of the image. A raw file editor that fails to adequately handle metadata compromises the user’s ability to organize, search, and archive their images effectively. For instance, a photographer reviewing a series of images taken at different locations relies on GPS metadata to accurately geotag and map their photographs. The absence of this data due to poor metadata handling would necessitate manual entry, increasing workflow complexity and the potential for errors.
The practical applications of metadata handling within a raw file editor are diverse. First, it enables efficient image organization and retrieval. By leveraging metadata, users can easily search for images based on specific criteria, such as date, location, or camera settings. Second, it facilitates copyright protection. Embedding copyright information within the metadata ensures that ownership details are associated with the image, providing legal recourse in cases of unauthorized use. Third, it streamlines collaboration. When sharing raw files with other editors or clients, the inclusion of metadata provides valuable context, allowing them to understand the photographer’s intentions and make informed editing decisions. For example, if a raw file contains metadata indicating that a specific lens profile was used, another editor can replicate that profile to maintain consistency. Furthermore, metadata can be used to automate certain editing tasks, such as applying specific presets based on camera settings. This automation streamlines workflow and reduces the risk of errors.
In conclusion, robust metadata handling is not merely a peripheral feature but a fundamental requirement for raw file editors on Android. The ability to preserve, manage, and utilize metadata enhances image organization, protects copyright, facilitates collaboration, and streamlines workflows. Challenges exist in ensuring compatibility across different raw file formats and metadata standards, requiring constant updates and careful implementation. Understanding the importance of metadata handling empowers users to maximize the potential of their raw files and maintain the long-term value of their photographic assets. Its integration with image editing is essential for a functional mobile application.
Frequently Asked Questions
The following addresses common inquiries regarding applications designed for the manipulation of unprocessed image data on the Android operating system.
Question 1: Why utilize a raw file editor on a mobile device instead of relying on standard JPEG processing?
Raw file editors offer significantly greater control over image parameters. They permit adjustments to exposure, white balance, and color with a level of precision unattainable with JPEG files, which have already undergone lossy compression and in-camera processing.
Question 2: Does the utilization of a raw file editor necessitate specialized hardware or expertise?
While advanced editing benefits from powerful hardware, many modern Android devices possess sufficient processing capabilities for basic raw file manipulation. A foundational understanding of photographic principles is advantageous but not strictly mandatory, as many applications offer intuitive interfaces and presets.
Question 3: What are the primary limitations encountered when editing raw files on Android devices?
Mobile devices typically possess smaller screens and less processing power than desktop computers, which can hinder precise adjustments and slow down processing times. Memory limitations may also restrict the size and number of files that can be simultaneously handled.
Question 4: Are raw file editors on Android compatible with all camera models and raw file formats?
Compatibility varies among applications. It is imperative to verify that a specific editor supports the raw file format generated by a user’s camera before committing to its use. DNG (Digital Negative) format offers broad compatibility.
Question 5: What impact does raw file editing have on device battery life?
Raw file processing is computationally intensive and can significantly reduce battery life. Users should anticipate shorter battery duration when engaged in prolonged editing sessions.
Question 6: Is it possible to revert to the original raw file after edits have been made within an Android editor?
Reputable raw file editors employ non-destructive editing techniques, preserving the original raw data. Edits are stored as metadata and can be undone or modified without altering the underlying file.
In essence, raw file editors on Android provide enhanced image control but necessitate awareness of hardware limitations and format compatibility. Non-destructive editing practices safeguard original data.
The subsequent sections will detail specific applications and their respective functionalities.
Optimizing “raw file editor for android” Workflow
The following comprises a set of best practices for maximizing the effectiveness of applications designed for manipulating unprocessed image data on the Android platform. Adherence to these guidelines facilitates streamlined workflows and superior image quality.
Tip 1: Verify Format Compatibility Prior to Import. Confirm that the application supports the specific raw file format generated by the camera. Attempting to process incompatible files will result in errors or data corruption. Refer to the application’s documentation or online resources for a comprehensive list of supported formats. For example, if the camera produces .CR2 files, explicitly ensure that the editor supports Canon’s CR2 raw format.
Tip 2: Utilize Non-Destructive Editing Principles. Ensure the application employs non-destructive editing techniques. This preserves the integrity of the original raw data, allowing for iterative adjustments without irreversible changes. Confirm that edits are stored as metadata, either within the file itself (e.g., DNG) or as separate XMP sidecar files.
Tip 3: Calibrate Display for Accurate Color Rendition. The accuracy of color adjustments is contingent upon the display’s calibration. Ideally, calibrate the Android device’s screen using a dedicated colorimeter. Alternatively, utilize built-in display calibration tools or profiles to minimize color casts and ensure consistent color representation.
Tip 4: Employ a Stylus for Precise Adjustments. The small screen size of mobile devices can hinder precise adjustments using fingers. Utilize a stylus to enhance accuracy when fine-tuning parameters such as exposure, white balance, and sharpening. This is particularly beneficial when working with intricate details or applying localized adjustments.
Tip 5: Optimize for Performance by Managing File Size. Raw files are inherently large and can strain device resources. Avoid processing excessively large files on underpowered devices. Consider resizing images or utilizing lower-resolution raw formats (if available) to improve performance.
Tip 6: Leverage Cloud Storage for Backup and Accessibility. Implement a cloud-based backup strategy for raw files and associated metadata. This ensures data security and facilitates access to images from multiple devices. Services such as Google Drive, Dropbox, or dedicated photo storage platforms offer seamless integration with Android devices.
Tip 7: Regularly Update the Application. Software developers release updates to resolve issues, improve functionality, and add new features, including increased raw format compatibility. A commitment to the foregoing recommendations empowers raw file editors on the Android platform, maximizing workflow efficiacy and achieving high-quality final results.
The subsequent article sections explore popular application options and their technical specifications.
Raw File Editors for Android
This exploration has delineated the function, capabilities, and considerations surrounding applications designed for the manipulation of unprocessed image data on the Android platform. From format compatibility and non-destructive editing to metadata handling and the nuances of sharpening and color grading, the ability to effectively process raw files on mobile devices represents a potent tool for photographers seeking enhanced control and creative flexibility.
As mobile technology continues its advancement, the potential of raw file editors on Android will undoubtedly expand, offering increasingly sophisticated features and processing power. Therefore, a continued understanding of the principles and techniques discussed herein remains crucial for effectively leveraging these tools and achieving optimal image quality in a mobile environment. The exploration of applications for raw file editor for android will continue to be updated for quality.
